Past Prompting: The Energy of Context Engineering

an LLM can see earlier than it generates a solution. This consists of the immediate itself, directions, examples, retrieved paperwork, instrument outputs, and even the prior dialog historical past.

Context has a big impact on reply high quality. For instance, for those who ask an LLM to jot down a SQL question with out offering the info schema, the end result will virtually actually be suboptimal. Worse, if the mannequin has no entry to the database in any respect, it could merely hallucinate a question that doesn’t work. Even when instruments can be found, the mannequin nonetheless wants additional effort and time to deduce the schema earlier than it might produce an accurate reply.

As a result of context performs such a central position in LLM-based functions, context engineering has emerged as a self-discipline targeted on systematically optimising what info goes right into a mannequin’s immediate. The aim is to construct “self-improving” methods that be taught from expertise with out counting on costly fine-tuning (retraining fashions and updating tens of millions of parameters).

Context engineering comes with a number of key benefits:

• it’s less expensive and doesn’t require specialised fine-tuning experience;
• context and directions stay clear, interpretable, and straightforward for people to change;
• iteration cycles are a lot quicker, since updates could be made immediately with out retraining or redeploying fashions;
• it’s extra agile, particularly when info must be forgotten for privateness or authorized causes.

With all these benefits, it’s not shocking that context engineering is gaining a lot consideration. What’s fascinating, although, is how shortly the approaches themselves are evolving. On this article, I’ll stroll via that evolution after which experiment with one of many newer frameworks for immediate optimisation: Agentic Context Engineering (ACE).

Evolution of context engineering approaches 

Context engineering didn’t seem in a single day. It has developed via a number of distinct levels.

The earliest stage was static prompting. Right here, prompts have been hand-crafted directions that by no means modified. Many of the effort went into traditional immediate engineering: fastidiously selecting wording, construction, and formatting to squeeze higher efficiency out of the mannequin. 

The following main step was dynamic retrieval. As a substitute of counting on a hard and fast immediate, methods started pulling in related info (paperwork, examples, or details) at inference time. Retrieval-Augmented Era (RAG) grew to become one of the common approaches on this class. By grounding responses in exterior information, RAG considerably improved accuracy and diminished hallucinations, particularly for knowledge-heavy duties.

Extra lately, the main focus has shifted towards self-improving contexts. Moderately than treating context as one thing that’s merely retrieved or injected, these approaches permit the system to replace and refine its personal context based mostly on previous efficiency. In different phrases, the immediate itself turns into adaptive, evolving via reflection and suggestions.

A variety of frameworks have emerged round this concept. Under are a number of the most influential ones.

• One of many earliest and most vital works is “Reflexion: Language Brokers with Verbal Reinforcement Studying” by Shinn et al. This analysis launched the concept language brokers can be taught from errors via pure language reflection reasonably than gradient-based updates. Reflexion brokers analyse suggestions from earlier makes an attempt, generate verbal reflections about what went unsuitable, and retailer these reflections in an episodic reminiscence buffer. These saved reflections then information higher decision-making in subsequent trials.
• One other necessary contribution is “TextGrad: Computerized Differentiation through Textual content” by Yuksekgonul et al. TextGrad borrows ideas from deep studying optimisation (resembling gradients, backpropagation, and gradient descent) however replaces numerical derivatives with pure language suggestions. On this framework, LLMs generate textual critiques describing how a variable ought to change to enhance the end result. These “textual gradients” are then propagated backwards via the system utilizing prompting, successfully performing a natural-language model of backpropagation throughout a compound AI system.
• The paper “GEPA: Reflective Immediate Evolution Can Outperform Reinforcement Studying” by Agrawal et al. takes a distinct angle by combining evolutionary algorithms with language-based reflection. Prompts are handled like organisms: they mutate, compete, and evolve underneath choice stress. Over time, better-performing prompts survive and propagate. This method is carried out in DSPy, and Hugging Face supplies a sensible information for making use of it in real-world use instances.
• Lastly, “Dynamic Cheatsheet: Take a look at-Time Studying with Adaptive Reminiscence” by Suzgun et al. explores test-time studying via persistent reminiscence. On this setup, a black-box LLM is given a pocket book the place it might write down helpful methods, patterns, and code snippets throughout inference. As a substitute of repeatedly rediscovering the identical insights, the mannequin accumulates and reuses data throughout duties. This adaptive reminiscence considerably improves efficiency with out requiring specific labels or human suggestions.

Agentic Context Engineering

Now that we’ve coated how context engineering has developed, let’s take a more in-depth have a look at Agentic Context Engineering (ACE), one of many newer approaches and the principle focus of this text. ACE is launched within the paper “Agentic Context Engineering: Evolving Contexts for Self-Enhancing Language Fashions” by Zhang et al., revealed in 2025.

The paper begins by figuring out two key issues with current self-improving context strategies.

• Brevity bias is the tendency for methods to oversimplify necessary particulars and regularly collapse towards quick, generic prompts. Whereas compact prompts are engaging, they typically lose the nuances that really drive good efficiency.
• Context collapse. When methods repeatedly rewrite your complete immediate, they have an inclination to neglect helpful data amassed earlier. Over time, this results in instability and regressions reasonably than regular enchancment.

To handle these points, the authors suggest Agentic Context Engineering (ACE), a framework designed for scalable and environment friendly context adaptation in each offline settings (resembling system immediate optimisation) and on-line eventualities (like test-time reminiscence adaptation). As a substitute of compressing data right into a single static immediate, ACE permits the mannequin to repeatedly evolve its context by accumulating profitable methods, reflecting on failures, and organising data in a structured approach. Conceptually, it resembles an AI assistant that improves over time by retaining detailed notes and refining its personal playbook.

On the core of ACE is an agentic studying loop that mirrors how people be taught via experimentation: strive, mirror, and consolidate. The framework consists of three parts:

• Generator, which produces reasoning trajectories whereas fixing duties;
• Reflector, which analyses successes and failures and distils actionable insights;
• Curator, which integrates these insights into the shared context as small, incremental updates.

Moderately than sustaining a single monolithic immediate, ACE organises context as a playbook made up of structured bullet factors. Every bullet incorporates metadata (resembling a novel identifier and counters monitoring how typically it has been useful or dangerous) in addition to content material representing a small, reusable unit of data. This is likely to be a normal technique, a domain-specific idea, or a standard failure mode.

The ACE workflow consists of a number of phases.

1. Era section. The Generator tackles new issues utilizing the present playbook, marking which bullets have been useful or deceptive.
2. Reflection section. The Reflector analyses the total trajectory, extracting classes from each successes and failures via iterative refinement.
3. Curation section. The Curator turns these insights into compact “delta” updates — new or modified bullets which can be merged into the present playbook utilizing light-weight, non-LLM logic.
4. Develop-and-refine section. New bullets are appended, current ones are up to date in place, and periodic deduplication removes redundancy utilizing semantic embeddings.

This design permits parallel processing of a number of updates and helps multi-epoch adaptation, the place the identical queries could be revisited to progressively strengthen the context over time.

Empirically, ACE delivers robust outcomes. On benchmark evaluations, it outperforms different self-improving context approaches, reaching a +10.6% enchancment on AI agent duties and a +8.6% acquire in specialised domains resembling finance. 

Past accuracy, ACE can be extra cost-efficient because of its incremental replace mechanism, exhibiting 83.6% decrease token prices in comparison with baseline strategies.

Collectively, these outcomes place ACE as a sensible and scalable step ahead in constructing self-improving LLM methods.

Utilizing ACE for banking intent information

The ACE framework appears promising on paper, so the subsequent step is to see the way it performs in apply. Luckily, the authors have shared an open-source implementation on GitHub, which provides us a strong place to begin.

Loading the information

To maintain the experiment targeted, I made a decision to use ACE to a classification process. I’m utilizing a publicly accessible dataset of banking intents launched by PolyAI (). This dataset displays a quite common real-world downside: figuring out buyer intent when somebody contacts buyer assist. Correct intent classification is vital for routing requests to the precise staff, triggering semi-automated responses, or just monitoring recurring points.

On this dataset, every buyer message (for instance, “I’m undecided why my card didn’t work”) must be mapped to a selected banking intent, resembling declined_card_payment. In whole, there are 77 distinct intent classes.

To maintain the experiment manageable, I sampled 500 examples from the dataset and break up them into coaching, check, and validation units. Under is the code used to load the info and create the splits.

full_df = pd.read_csv('./poly_ai_banking_data/prepare.csv')

# params
total_number_of_samples = 500 
train_share = 0.5
test_share = 0.4
val_share = 0.1

sample_df = full_df.pattern(n=total_number_of_samples, random_state=42)
  .reset_index(drop=True)

random.seed(42)
sample_df['group'] = random.selections(['train', 'test', 'val'], 
  weights=(train_share, test_share, val_share), ok=total_number_of_samples)

train_df = sample_df[sample_df['group'] == 'prepare'].reset_index(drop=True)
test_df = sample_df[sample_df['group'] == 'check'].reset_index(drop=True)
val_df = sample_df[sample_df['group'] == 'val'].reset_index(drop=True)

Extending ACE to banking intent information

The following step is to increase the ACE framework so it might work with our banking intent dataset. Luckily, the authors present a detailed information that makes this course of comparatively easy.

Along with plugging within the new dataset, I made a few small modifications to the core framework to assist Anthropic fashions and configurable temperature settings. You could find the whole, modified model of the code on GitHub.

Making ready the info

The very first thing we have to do is put together the dataset in a format that ACE expects. I saved the coaching, validation, and check splits as CSV recordsdata underneath banking/information. Every instance incorporates:

• textual content: the shopper assist message,
• class: the goal intent label we wish to predict,
• group: an auxiliary area indicating whether or not the instance belongs to the prepare, check, or validation set.

The group area received’t be used later by the framework itself, however it’s handy for dataset administration and reproducibility.

Right here’s what the info format appears like.

textual content,class,group
Is it attainable for me to alter my PIN quantity?,change_pin,check
What's the $1 transaction on my account?,extra_charge_on_statement,check
How a lot does high up charges price?,top_up_by_card_charge,check
I dwell within the EU - can I get a card?,country_support,check

Subsequent, we have to inform ACE the place to search out every break up. That is completed by specifying dataset paths in banking/information/task_config.json.

{
  "banking": {
    "train_data": "./banking/information/prepare.csv",
    "val_data": "./banking/information/val.csv",
    "test_data": "./banking/information/check.csv"
  }
}

Implementing the DataProcessor

To combine a brand new process, the framework requires a customized DataProcessor module. In response to the information, this includes implementing three core strategies: process_task_data, answer_is_correct and evaluate_accuracy.

As well as, we’d like a helper perform to load the uncooked information from disk. Let’s begin with that.

Under is the implementation of the data-loading perform. It reads a CSV file, validates its existence, and converts every row right into a dictionary that the remainder of the pipeline can work with.

def load_data(data_path: str) -> Checklist[Dict[str, Any]]:
  """
  Load and course of information from a CSV file.
  
  Anticipated CSV format: textual content,class,group (with header)
  
  Args:
    data_path: Path to the CSV file
      
  Returns:
    Checklist of dictionaries containing the info
  """
  if not os.path.exists(data_path):
    increase FileNotFoundError(f"Information file not discovered: {data_path}")
  
  information = []
  with open(data_path, 'r', encoding='utf-8') as f:
    reader = csv.DictReader(f)
    for row in reader:
      information.append({
        'textual content': row['text'],
        'class': row['category'],
        'group': row.get('group', '')
      })
  
  print(f"Loaded {len(information)} samples from {data_path}")
  return information

With the data-loading perform in place, we are able to transfer on to implementing the remaining DataProcessor strategies.

The principle goal of process_task_data is to transform the uncooked dataset into ACE’s standardised enter format.

ACE expects every instance to comprise three fields: context, query, and goal. In our case, the mapping is pretty easy. We map the intent class on to goal, and we go away context empty since there’s no further background info wanted for classification.

Crucial half right here is the query. We added additional context to make it clear to the LLM that it ought to classify the question reasonably than reply questions straight, whereas additionally offering the listing of accessible subjects to information an LLM’s response.

def process_task_data(self, raw_data: Checklist[Dict]) -> Checklist[Dict]:
  """
  Convert uncooked CSV information into standardized format for ACE.
  
  Args:
    raw_data: Uncooked information loaded from CSV (listing of dicts with 'textual content', 'class')
      
  Returns:
    Checklist of dicts with keys: 'context', 'query', 'goal'
  """
  processed_data = []
  
  # Collect the listing of subjects to incorporate into the query
  topics_list = ", ".be a part of(self.allowed_topics)
  
  for merchandise in raw_data:
    customer_query = merchandise.get('textual content', '')
    ground_truth_topic = merchandise.get('class', '')
    
    # The query supplies the classification process instruction
    query = (
      f"Classify the next banking buyer assist question into one of many predefined subjects.nn"
      f"Buyer Question: {customer_query}nn"
      f"Accessible Subjects: {topics_list}nn"
      f"Reply with ONLY the subject identify, nothing else."
    )
    
    processed_item = {
      "context": "",  # No further context wanted
      "query": query,
      "goal": ground_truth_topic,
      "others": {
        "original_text": customer_query,
        "process": self.task_name,
      }
    }
    
    processed_data.append(processed_item)
  
  return processed_data

The following methodology, answer_is_correct, checks whether or not a mannequin’s prediction matches the bottom reality label. Since we explicitly instruct the LLM to reply with solely the class identify, a easy case-insensitive string comparability is adequate right here.

def answer_is_correct(self, predicted: str, ground_truth: str) -> bool:
  """
  Test if the expected matter matches the bottom reality.
  Makes use of easy case-insensitive comparability.
  
  Args:
    predicted: Mannequin's predicted matter
    ground_truth: Floor reality matter
      
  Returns:
    bool: True if prediction is appropriate, False in any other case
  """
  return predicted.decrease().strip() == ground_truth.decrease().strip()

The ultimate methodology we have to implement is evaluate_accuracy, which computes general classification accuracy throughout a number of predictions. There’s nothing fancy happening right here. We merely calculate the fraction of instances the place answer_is_correct(prediction, ground_truth) returns True.

def evaluate_accuracy(self, predictions: Checklist[str], ground_truths: Checklist[str]) -> float:
  """
  Calculate classification accuracy throughout a number of predictions.
  
  Args:
    predictions: Checklist of mannequin predictions
    ground_truths: Checklist of floor reality subjects
      
  Returns:
    Accuracy as a float between 0 and 1
  """
  if len(predictions) != len(ground_truths):
    increase ValueError("Predictions and floor truths will need to have similar size")
  
  if not predictions:
    return 0.0
  
  appropriate = sum(
    1 for pred, reality in zip(predictions, ground_truths)
    if self.answer_is_correct(pred, reality)
  )
  
  return appropriate / len(predictions)

Placing collectively the workflow script

With the DataProcessor in place, the subsequent step is to assemble a complete run script for ACE. I created a run_ace_workflow script that accepts a number of key arguments:

• api_provider selects the language mannequin API to make use of ('anthropic', 'openai', 'collectively', or 'sambanova'), defaulting to 'anthropic'.
• generator_model specifies the mannequin for the Generator agent (default: 'claude-haiku-4-5').
• reflector_model specifies the mannequin for the Reflector agent (default: 'claude-sonnet-4-5').
• curator_model specifies the mannequin for the Curator agent (default: 'claude-sonnet-4-5').
• max_train and max_test are non-compulsory limits on the prepare and check set sizes, helpful for fast experiments or debugging.

Let’s focus on how this script truly works. The script begins by loading the banking intent information and initialising the DataProcessor. Right here’s the helper perform I wrote for this.

def load_banking_data(max_train=None, max_test=None):
  """Load and course of banking dataset."""
  from banking.data_processor import DataProcessor, load_data
  
  base_path = os.path.dirname(__file__)
  data_path = os.path.be a part of(base_path, "information")
  
  # Load uncooked information
  train_raw = load_data(os.path.be a part of(data_path, "prepare.csv"))
  val_raw = load_data(os.path.be a part of(data_path, "val.csv"))
  test_raw = load_data(os.path.be a part of(data_path, "check.csv"))
  
  # Restrict samples if specified
  if max_train:
    train_raw = train_raw[:max_train]
    val_raw = val_raw[:max(max_train // 4, 10)]
  if max_test:
    test_raw = test_raw[:max_test]
  
  # Course of information
  processor = DataProcessor(task_name="banking")
  train_samples = processor.process_task_data(train_raw)
  val_samples = processor.process_task_data(val_raw)
  test_samples = processor.process_task_data(test_raw)
  
  return train_samples, val_samples, test_samples, processor

train_samples, val_samples, test_samples, processor = load_banking_data(
  max_train=args.max_train,
  max_test=args.max_test
)

The following step is to outline a playbook template. That is necessary as a result of the present ACE implementation can’t dynamically create new sections, so we predefine the construction to information the mannequin. Right here’s the template I used for the banking area.

BANKING_PLAYBOOK_TEMPLATE = """
## GENERAL
## CLASSIFICATION PRINCIPLES
## CATEGORY DISAMBIGUATION
## BANKING DOMAIN KNOWLEDGE
## COMMON PATTERNS
## HANDLING AMBIGUOUS QUERIES
## COMMON MISTAKES TO AVOID
## OTHERS
"""

With the info and template prepared, we are able to initialise the ACE object with the principle parameters.

ace_system = ACE(
  api_provider=args.api_provider,
  generator_model=args.generator_model,
  reflector_model=args.reflector_model,
  curator_model=args.curator_model,
  max_tokens=4096,
  initial_playbook=BANKING_PLAYBOOK_TEMPLATE,
  use_bulletpoint_analyzer=True, # enabling deduplication of bullet factors within the playbook
  generator_temperature=0.1, # prioritising consistency for generator
  reflector_temperature=0.7, # prioritising creativity for reflector and curator
  curator_temperature=0.7,
)

Lastly, we outline a perform to run the ACE coaching workflow, which incorporates preliminary analysis, iterative reflection, curation, and closing analysis.

def run_ace_training(ace_system, train_samples, val_samples, test_samples, processor, results_dir):
  """Prepare ACE to enhance the playbook (consists of preliminary and closing evaluations)."""
  config = {
    'num_epochs': 1,
    'max_num_rounds': 3,  # max reflection rounds per pattern
    'curator_frequency': 5,  # run curator each 5 steps
    'eval_steps': max(len(train_samples) // 10, 10),  # consider 10 instances throughout coaching
    'save_steps': max(len(train_samples) // 10, 10),
    'playbook_token_budget': 80000,
    'task_name': 'banking_ace',
    'json_mode': False,
    'no_ground_truth': False,
    'save_dir': os.path.be a part of(results_dir, "coaching"),
    'test_workers': 10,
  }
  
  outcomes = ace_system.run(
    mode='offline',
    train_samples=train_samples,
    val_samples=val_samples,
    test_samples=test_samples,
    data_processor=processor,
    config=config
  )
  
  # Extract outcomes
  initial_acc = outcomes.get('initial_test_results', {}).get('accuracy', 0)
  final_acc = outcomes.get('final_test_results', {}).get('accuracy', 0)
  training_results = outcomes.get('training_results', {})
  
  return ace_system.best_playbook, outcomes

best_playbook, training_results = run_ace_training(
  ace_system, train_samples, val_samples, test_samples, 
  processor, results_dir
)

And that’s it! That’s all of the core logic we have to run ACE. I’ve added some logging on high of the workflow for comfort, however it’s not important to the principle performance.

Outcomes

Let’s check out the outcomes and see how the whole lot comes collectively. First, take a look at the very best playbook, which you will discover at outcomes/banking_{dt}/best_playbook.txt. The playbook is organised into itemised bullets, grouped in response to the classes we outlined in our preliminary template. Every bullet incorporates detailed directions and techniques, together with metadata exhibiting how typically it was marked useful or dangerous. This construction makes it straightforward to see which subjects and techniques the system discovered most helpful throughout coaching.

## GENERAL
## CLASSIFICATION PRINCIPLES
[cls-00001] useful=1 dangerous=0 :: Temporal indicators like 'was in a position to earlier than', 'labored beforehand', or 'used to work' are robust indicators that the problem is particular to the present transaction reasonably than a normal system functionality downside. These phrases recommend a change in standing for a selected entity (beneficiary, card, account) reasonably than general performance.
[cls-00002] useful=18 dangerous=4 :: Apply specificity hierarchy: when a number of classes may apply, select probably the most particular one which matches the contextual clues. For instance, beneficiary_not_allowed (particular to recipient) is extra particular than declined_transfer (normal failure).
[cls-00009] useful=0 dangerous=3 :: Specificity hierarchy works bidirectionally: select particular classes when contextual clues level to a selected transaction sort, however use normal classes (like 'extra_charge_on_statement') when the question lacks adequate context to find out the particular nature of the transaction. Do not power specificity when the shopper's question is inherently normal.
[cls-00017] useful=5 dangerous=1 :: Course of-oriented vs Standing-tracking distinction: Differentiate between questions on HOW to acquire/purchase one thing (process-oriented) versus questions on WHEN one thing will arrive or WHETHER it has arrived (status-tracking). Course of questions concentrate on the steps and parts wanted, whereas standing questions concentrate on timing and supply affirmation. Use this distinction to decide on between acquisition classes and monitoring/arrival classes.
## CATEGORY DISAMBIGUATION
[dis-00003] useful=1 dangerous=0 :: declined_transfer vs beneficiary_not_allowed: If the shopper mentions they might switch earlier than however all of a sudden can't, this strongly signifies beneficiary_not_allowed (recipient is blocked/restricted) reasonably than declined_transfer (normal switch failure resulting from funds, limits, or system errors).
[dis-00011] useful=11 dangerous=0 :: pending_* vs failed_* vs declined_*: Transaction state is vital for classification. 'Hasn't gone via but' or 'taking too lengthy' = pending state. 'Did not work', 'was declined', or 'was rejected' = failed/declined state. 'Cash got here again' or 'was returned' = reverted state. Match the class to the precise transaction state described.
[dis-00012] useful=0 dangerous=1 :: country_support vs supported_cards_and_currencies: Queries about geographic availability ('which nations', 'the place can I', 'what areas') must be categorised as 'country_support'. In distinction, 'supported_cards_and_currencies' is for questions on card sorts (Visa, Mastercard) and forex choices, not geographic availability.
[dis-00014] useful=2 dangerous=0 :: Money withdrawal points: Distinguish by transaction state and end result: 'pending_cash_withdrawal' (not accomplished but, nonetheless processing), 'declined_cash_withdrawal' (rejected, no money obtained), 'cash_withdrawal_not_recognised' (buyer does not recall the transaction), and 'wrong_amount_of_cash_received' (transaction accomplished however incorrect quantity disbursed). If money was obtained however the quantity was unsuitable, use probably the most particular class: wrong_amount_of_cash_received.
[dis-00015] useful=3 dangerous=3 :: card_arrival vs get_physical_card: Distinguish between status-tracking questions (card_arrival) and process-acquisition questions (get_physical_card). 'card_arrival' is for monitoring current orders ('Has my card arrived?', 'The place is my card?'). 'get_physical_card' encompasses your complete technique of acquiring a bodily card together with all parts like PIN ('The place can I discover my PIN?', 'How do I get my card and PIN?'). Questions on lacking PINs with 'have not gotten it but' point out the shopper is within the acquisition course of, not simply monitoring supply.
[dis-00021] useful=1 dangerous=0 :: card_payment_not_recognised vs extra_charge_on_statement: When a buyer mentions a 'fee' they do not acknowledge or did not make ('fee I by no means submitted', 'fee I did not authorize'), classify as 'card_payment_not_recognised' as a result of 'fee' is a selected transaction sort. Use 'extra_charge_on_statement' solely when the shopper describes sudden quantities, charges, or prices WITHOUT specifying the transaction sort (e.g., 'I see an additional $5 on my assertion', 'there is a unusual cost' with out mentioning fee/switch/withdrawal).
[dis-00024] useful=0 dangerous=1 :: Charge/cost class specificity: When prospects ask about charges or prices, prioritize transaction-type-specific charge classes over 'extra_charge_on_statement'. If the question mentions a selected transaction sort (switch, fee, withdrawal, top-up), use the corresponding particular charge class: 'transfer_fee_charged' for switch charges, 'card_payment_fee_charged' for fee charges, 'atm_fee_charged' for withdrawal charges, 'top_up_fee' for top-up charges. Reserve 'extra_charge_on_statement' just for charge queries the place no particular transaction sort is talked about (e.g., 'Why is there an additional $5 cost?' with out context).
[dis-00026] useful=0 dangerous=0 :: receiving_money vs transfer_into_account: Distinguish between passive receipt and energetic switch. 'receiving_money' is for queries about receiving funds FROM one other get together (passive, initiated by sender). 'transfer_into_account' is for queries concerning the buyer initiating a switch TO add funds to their very own account (energetic, self-initiated). Context clues: empty/low steadiness + asking about transfers = seemingly transfer_into_account. Questions on 'can I switch funds' within the context of needing so as to add cash = transfer_into_account, not receiving_money.
[dis-00029] useful=0 dangerous=0 :: beneficiary_not_allowed vs declined_transfer: When a question explicitly mentions 'beneficiary' or 'recipient' mixed with restriction language ('not allowed', 'blocked', 'restricted', 'can't add', 'unable so as to add'), classify as 'beneficiary_not_allowed' even with out temporal indicators. The mix of the particular banking entity time period (beneficiary/recipient) with restriction language is a robust direct sign for recipient-level restrictions reasonably than normal switch failures.
## BANKING DOMAIN KNOWLEDGE
[bank-00006] useful=0 dangerous=0 :: In banking, when a beforehand profitable switch all of a sudden fails, widespread causes embody: beneficiary being flagged/blocked by fraud methods, beneficiary account restrictions, or beneficiary being faraway from allowed listing. These are distinct from normal switch declines resulting from inadequate funds or system errors.
[bank-00008] useful=0 dangerous=6 :: Small sudden quantities (like £1, £0.01) showing on statements typically point out authorization holds, verification prices, or miscellaneous charges. When prospects query these with out further context, they need to be categorised as 'extra_charge_on_statement' reasonably than extra particular transaction sorts.
[bank-00018] useful=0 dangerous=0 :: 'card_swallowed' is the banking trade time period for ATM card retention eventualities the place the machine retains/retains the shopper's card. This is applicable when playing cards are caught, will not come out, or are held by the ATM, whatever the particular phrasing utilized by the shopper.
[bank-00020] useful=10 dangerous=4 :: Banking terminology has a specificity hierarchy for transaction references. Particular transaction sort key phrases embody: 'fee' (card funds), 'switch' (cash transfers), 'withdrawal' (money withdrawals), 'top-up' (account funding), 'direct debit', 'standing order'. Generic phrases embody: 'cost', 'quantity', 'transaction', 'charge'. When a buyer makes use of a selected transaction sort key phrase, it supplies adequate context to categorise into transaction-type-specific classes reasonably than normal classes.
## COMMON PATTERNS
[pat-00004] useful=0 dangerous=0 :: Sample: 'It labored earlier than, now it does not' + switch context = seemingly beneficiary-level restriction reasonably than system-level decline. The earlier success signifies the account and switch mechanism are practical, pointing to a selected restriction on the present recipient.
[pat-00007] useful=3 dangerous=6 :: Sample: Buyer describes transaction as 'unusual', 'sudden', 'unexplained', or asks 'what is that this cost' on their assertion with out offering particular transaction sort context (switch, fee, withdrawal, and so on.) = classify as 'extra_charge_on_statement'. That is the suitable normal class when the character of the cost is unclear.
[pat-00010] useful=8 dangerous=1 :: Sample: Phrases like 'hasn't gone via but', 'nonetheless ready', 'not accomplished', or 'nonetheless pending' point out a transaction in PENDING state, not a FAILED state. Select 'pending_*' classes over 'failed_*' or 'declined_*' classes when these language cues are current.
[pat-00013] useful=0 dangerous=2 :: Sample: Questions with geographic scope indicators like 'which nations', 'the place can I', 'what areas', or 'in what places' are asking about service availability by geography = classify as 'country_support'. The core intent is knowing geographic attain of companies.
[pat-00016] useful=2 dangerous=9 :: Sample: 'The place can I discover' or 'How do I get' phrasing signifies process-oriented questions in search of details about acquiring or buying one thing, not status-tracking questions. These ought to usually map to acquisition/setup classes (like 'get_physical_card') reasonably than supply/monitoring classes (like 'card_arrival' or 'card_delivery_estimate').
[pat-00019] useful=0 dangerous=0 :: Sample: Phrases indicating a card is bodily retained by an ATM ('card caught in ATM', 'card will not come out', 'ATM stored my card', 'get my card out of ATM', 'retrieve card from machine') must be categorised as 'card_swallowed'. The important thing indicator is the cardboard being bodily held/retained by the machine reasonably than different card points like injury, loss, or performance issues.
[pat-00022] useful=1 dangerous=0 :: Sample: Particular transaction sort key phrase + 'not acknowledged'/'did not make'/'by no means submitted' = use transaction-type-specific 'not_recognised' class. Examples: 'fee I did not make' → card_payment_not_recognised; 'switch I do not acknowledge' → transfer_not_received_by_recipient or associated switch subject; 'withdrawal I by no means made' → cash_withdrawal_not_recognised. The presence of a selected transaction sort key phrase (fee, switch, withdrawal) is adequate context to keep away from normal classes.
[pat-00025] useful=1 dangerous=0 :: Sample: Transaction sort key phrase + timing query ('how lengthy', 'when will', 'how a lot time') + geographic point out = prioritize transaction-specific timing class (e.g., 'transfer_timing', 'card_delivery_estimate'). Deal with geographic mentions as contextual details about the transaction origin/vacation spot except the question explicitly asks about service availability ('which nations', 'the place can I exploit', 'is it accessible in'). Instance: 'switch from China, how lengthy?' → 'transfer_timing' (not 'country_support').
[pat-00027] useful=0 dangerous=0 :: Sample: Account steadiness context + switch inquiry = intent so as to add funds. When a buyer mentions their account is empty/has no funds/wants cash AND asks about transferring, they're asking about transferring funds INTO their account (transfer_into_account), not about receiving cash from others (receiving_money). The account state supplies vital context for disambiguating transfer-related intents.
## HANDLING AMBIGUOUS QUERIES
## COMMON MISTAKES TO AVOID
[err-00005] useful=2 dangerous=0 :: Do not default to normal classes (like declined_transfer) when temporal context ('was in a position to earlier than') suggests a extra particular subject. The temporal change is a key discriminator that usually factors to entity-specific restrictions (beneficiary, card, account) reasonably than normal failures.
[err-00023] useful=2 dangerous=0 :: Do not default to 'extra_charge_on_statement' when the shopper mentions a selected transaction sort (fee, switch, withdrawal, top-up) they do not acknowledge. 'extra_charge_on_statement' must be reserved for really ambiguous instances the place no transaction sort is specified. When a buyer says 'fee I by no means made', the phrase 'fee' supplies adequate context to make use of 'card_payment_not_recognised' as a substitute of the generic 'extra_charge_on_statement'.
[err-00028] useful=0 dangerous=0 :: Do not apply sample guidelines or area data which can be irrelevant to the question. If a question has no geographic indicators, do not apply geographic patterns. If there is no point out of charges, do not apply fee-related guidelines. Give attention to guidelines that straight match the semantic content material and context of the shopper's question reasonably than greedy for any relevant rule. Irrelevant rule utility results in misclassification.
## OTHERS

For a deeper have a look at how every agent operates, you possibly can discover the detailed execution logs at outcomes/banking_{dt}/coaching/ace_run_{dt}/detailed_llm_logs . I extremely advocate looking these logs. On the very least, skim via the prompts and see how the Generator, Reflector, and Curator work together. It’s a good way to know how ACE evolves the context step-by-step.

In fact, probably the most fascinating metric is accuracy. You could find the preliminary and closing check leads to outcomes/banking_{datetime}/coaching/initial_test_results.json and outcomes/banking_{datetime}/coaching/final_test_results.json.

# preliminary outcomes 
{
  "test_results": {
    "accuracy": 0.7512437810945274,
    "appropriate": 151,
    "whole": 201,
    "no_answer": 0
  },
  "error_log": {
    "accuracy": 0.7512437810945274,
    "errors": [
      {
        "index": 2,
        "prediction": "declined_card_payment",
        "ground_truth": "declined_transfer"
      },
      {
        "index": 9,
        "prediction": "top_up_limits",
        "ground_truth": "automatic_top_up"
      },
      {
        "index": 7,
        "prediction": "transfer_not_received_by_recipient",
        "ground_truth": "balance_not_updated_after_cheque_or_cash_deposit"
      },
      ...
    ]
  }
}

# closing outcomes 
{
  "test_results": {
    "accuracy": 0.736318407960199,
    "appropriate": 148,
    "whole": 201,
    "no_answer": 0
  },
  "error_log": {
    "accuracy": 0.736318407960199,
    "errors": [
      {
        "index": 9,
        "prediction": "top_up_limits",
        "ground_truth": "automatic_top_up"
      },
      {
        "index": 2,
        "prediction": "declined_card_payment",
        "ground_truth": "declined_transfer"
      },
      {
        "index": 7,
        "prediction": "pending_transfer",
        "ground_truth": "balance_not_updated_after_cheque_or_cash_deposit"
      },
      ...
    ]
  }
}

The outcomes, admittedly, aren’t very spectacular. The truth is, accuracy barely dropped after optimisation, from 75.1% to 73.6%. However even unfavorable outcomes can train us one thing helpful.

There are a number of seemingly explanation why ACE didn’t present a lot profit on this case:

• Restricted information per class. We solely had 248 coaching examples, 201 check examples, and 51 validation examples. Nonetheless, our process concerned 77 completely different classes. With so few examples per class, the mannequin merely could not have had sufficient information to be taught significant distinctions.
• Small and unrepresentative validation set. With solely 51 examples, the validation set won’t have captured the total range of buyer queries, making it troublesome for ACE to generate helpful reflections and enhancements.
• Process complexity. Our use case is comparatively easy. Because the authors notice, ACE tends to shine in eventualities with giant quantities of extremely specialised area data or extra advanced agentic workflows, the place reflection and iterative context refinement can considerably enhance efficiency.

Utilizing ACE for code technology

Inspired by the earlier experiment, I made a decision to offer ACE one other strive. This time on the Principally Fundamental Python Issues dataset (accessible underneath cc-by-4.0 license). Hopefully, the outcomes can be extra promising with a code technology process.

Information overview

Every instance within the dataset incorporates three key parts:

• Query, for instance, “Write a perform to reverse phrases in a given string.”
• Floor reality implementation — Python reference code. For instance, for the query above

def reverse_words(s):
  return ' '.be a part of(reversed(s.break up()))

• Take a look at instances  are  assertions to validate the generated code, resembling

[
    assert reverse_words("python program")==("program python"),
    assert reverse_words("java language")==("language java"),
    assert reverse_words("indian man")==("man indian")
]

Including a brand new process to the ACE framework

We will observe comparable steps to increase the ACE framework to deal with coding duties. I received’t go into all of the implementation particulars right here, since you will discover the total code on GitHub. Nonetheless, it’s value highlighting the important thing variations in comparison with the banking intent instance.

Coding duties are inherently extra advanced. Within the banking intent case, the mannequin outputs a single class out of 77, which is straightforward to match straight with the bottom reality. In code technology, nevertheless, the LLM can produce arbitrary code, so we can’t merely verify for actual matches. As a substitute, we have to run exams to find out whether or not the generated resolution is appropriate.

# banking

def answer_is_correct(self, predicted: str, ground_truth: str) -> bool:
  return predicted.decrease() == ground_truth.decrease()

# coding 
def answer_is_correct(self, predicted: str, ground_truth: str, 
                    test_list: Checklist[str], idx: int, save_dir: str) -> bool:
  code = extract_code_from_response(predicted)
  end result = execute_code_with_tests(code, test_list, timeout=5)
  return end result['success']

Due to this added complexity, I needed to implement a number of enhancements within the DataProcessor for code technology:

• Code extraction. LLMs typically embody additional context across the code, resembling Markdown formatting (```python ...```). We have to clear and extract the code to make sure it might compile accurately.
• Protected execution. Since we run the generated code to confirm correctness, it’s necessary to implement primary security measures, resembling timeouts and remoted execution environments.
• Offering full context. It’s essential to incorporate all needed info within the query. If we simply ask the LLM to generate code, it’s unlikely to go the exams as a result of it received’t be clear what perform identify or signature is anticipated. That’s why it’s essential to offer all needed particulars within the query when standardising the info within the process_task_data perform.

query = (
  f"Write a Python perform to unravel the next downside:nn"
  f"Downside: {problem_text}nn"
  f"Your code should go the next check instances:n"
  f"{test_cases_formatted}nn"
  f"Essential: The check instances will likely be executed in opposition to your code. "
  f"Be certain that your perform identify and signature match what the exams count on.nn"
  f"Reply with ONLY the Python code, no explanations."
)

Within the authentic ACE implementation, the Reflector in contrast generated code straight with the bottom reality, which works for classification duties. For coding, nevertheless, this method doesn’t make sense: a number of appropriate options can exist, and optimising for code that “appears comparable” to the reference doesn’t assure it’s going to go the exams.

To handle this, I carried out a brand new methodology, get_test_feedback, which supplies the Reflector with precise check execution outcomes and error messages. The check output turns into the first sign for correctness, giving rather more informative suggestions than easy code comparability.

def get_test_feedback(self, predicted: str, ground_truth: str, test_list: Checklist[str] = None) -> str:
  """
  Get detailed check execution suggestions for the reflector.
  
  This methodology supplies the reflector with precise check outcomes and error messages,
  which is extra informative than simply evaluating generated code with floor reality.
  The check output is the first sign for correctness in code technology duties.
  
  Args:
      predicted: Mannequin's predicted code
      ground_truth: Floor reality code (reference solely, not used for analysis)
      test_list: Checklist of check assertions to run
      
  Returns:
      str: Detailed suggestions string with check execution outcomes
  """
  if test_list is None:
      return "No check instances offered - can't consider code."
  
  # Extract code from response if wanted
  code = extract_code_from_response(predicted)
  
  # Execute code with exams
  end result = execute_code_with_tests(code, test_list, timeout=self.timeout)
  
  # Construct detailed suggestions
  feedback_parts = []
  
  if end result['success']:
    feedback_parts.append(f"✓ All {end result['total']} exams PASSED")
    feedback_parts.append("nTest instances executed efficiently:")
    for i, check in enumerate(test_list, 1):
        feedback_parts.append(f"  {i}. {check} ✓")
  else:
    feedback_parts.append(f"✗ Assessments FAILED: {end result['passed']}/{end result['total']} exams handed")
    
    if end result['timeout']:
      feedback_parts.append("n⏱ TIMEOUT: Code execution exceeded time restrict")
    
    if end result['errors']:
      feedback_parts.append("n--- ERROR DETAILS ---")
      for error in end result['errors']:
        feedback_parts.append(f"  • {error}")
    
    # Present which exams handed vs failed
    feedback_parts.append("n--- TEST RESULTS ---")
    for i, check in enumerate(test_list, 1):
      # Test if this particular check seems in errors
      test_failed = any(f"Take a look at {i}" in err for err in end result.get('errors', []))
      standing = "✗ FAILED" if test_failed else "✓ handed"
      feedback_parts.append(f"  {i}. {check} - {standing}")
  
  # Add extracted code for reference
  feedback_parts.append("n--- EXTRACTED CODE ---")
  feedback_parts.append(code)
  
  return "n".be a part of(feedback_parts)

Alongside this new methodology, I created a devoted Reflector immediate tailor-made for code technology. Its focus is on check outcomes, not line-by-line code comparability.

You're an knowledgeable code reviewer and educator. Your job is to research why generated code handed or failed check instances, and establish patterns that result in appropriate or incorrect options.

**IMPORTANT: Take a look at execution outcomes are the PRIMARY sign for correctness.**
- The code is appropriate if and provided that ALL exams go
- Do NOT examine implementations line-by-line with the reference - completely different implementations could be equally appropriate
- Give attention to understanding WHY exams handed or failed based mostly on the code's logic

**Directions:**
- First, study the Take a look at Execution Outcomes to find out if the code is appropriate
- If exams FAILED: Analyze what prompted the failure (syntax errors, logic errors, edge instances, unsuitable algorithm)
- If exams PASSED: Determine what the mannequin did nicely that led to success
- The "Doable Implementation" is simply ONE technique to clear up the issue - the mannequin's method could also be completely different however equally legitimate
- Present actionable insights for enhancing code technology sooner or later
- Tag bulletpoints as useful/dangerous/impartial based mostly on whether or not they contributed to passing exams

Your output must be a json object, which incorporates the next fields:
  - reasoning: analyze the check outcomes and the code's logic, clarify why exams handed/failed
  - error_identification: if exams failed, what particular subject prompted the failure? If exams handed, state "No errors - all exams handed"
  - root_cause_analysis: what underlying idea or sample led to success or failure?
  - correct_approach: what coding technique or sample must be used for comparable issues?
  - key_insight: what precept must be remembered for future code technology duties?
  - bullet_tags: an inventory of json objects with bullet_id and tag for every bulletpoint

**Query:**
{}

**Mannequin's Reasoning Hint:**
{}

**Mannequin's Generated Code:**
{}

**Doable Implementation (Reference Solely - NOT the one appropriate resolution):**
{}

**Take a look at Execution Outcomes (PRIMARY SIGNAL):**
{}

**A part of Playbook that is utilized by the generator to reply the query:**
{}

**Reply on this actual JSON format:**
{{
  "reasoning": "[Analyze test results and code logic - why did tests pass or fail?]",
  "error_identification": "[What caused test failures? Or 'No errors - all tests passed']",
  "root_cause_analysis": "[What concept/pattern led to success or failure?]",
  "correct_approach": "[What coding strategy works for this type of problem?]",
  "key_insight": "[What principle should be remembered for future code generation?]",
  "bullet_tags": [
    {{"id": "code-00001", "tag": "helpful"}},
    {{"id": "code-00002", "tag": "harmful"}}
  ]
}}

This coding-specific Reflector is mechanically used each time the duty identify incorporates "coding".

Outcomes

Lastly, I ran the immediate optimisation course of on a dataset of 500 samples, break up into prepare, check, and validation units. This time, the outcomes are rather more promising: accuracy improved considerably from 71.1% to 87.1%. On this case, ACE clearly helped optimise the prompts and information the mannequin towards appropriate options.

Taking a look at the very best playbook, it’s fairly in depth. Lots of the most useful patterns are normal ideas, resembling:

• Write the best appropriate, Pythonic resolution first,
• Deal with check instances because the true specification,
• Confirm correctness earlier than any additional optimisation.

On the similar time, the playbook additionally consists of very particular steering, for instance, detailed directions for duties like GCD calculations.

General, this reveals that ACE can successfully seize each high-level methods and task-specific suggestions.

## GENERAL
## COMMON MISTAKES TO AVOID
[err-00003] useful=5 dangerous=0 :: Do not add pointless complexity to recursive algorithms. For instance, in GCD implementations, specific min/max logic or particular instances for checking if a worth equals 1 are redundant when utilizing the usual Euclidean algorithm.
[err-00007] useful=0 dangerous=0 :: Do not assume downside constraints match your algorithm's mathematical conditions. For instance, Fermat's Little Theorem for modular inverse requires a PRIME modulus - confirm the issue ensures this earlier than utilizing pow(a, p-2, p). If constraints aren't specified, select extra normal algorithms.
## OTHERS
## CODE GENERATION PRINCIPLES
[cgp-00002] useful=41 dangerous=2 :: Want minimal, mathematically sound implementations over advanced ones. Keep away from including pointless preprocessing logic (like min/max) or particular case checks when the core algorithm naturally handles all eventualities.
[cgp-00012] useful=91 dangerous=2 :: At all times guarantee generated code is syntactically full earlier than finalizing output. Confirm all opened brackets, braces, and parentheses are correctly closed, and all statements are absolutely fashioned. Incomplete code technology (truncation mid-statement) causes syntax errors that forestall execution no matter algorithmic correctness.
[cgp-00020] useful=6 dangerous=0 :: When an issue explicitly requires utilizing lambda features, combine them naturally with Python's practical programming instruments (map, filter, scale back, sorted with key parameter). Do not power lambda utilization the place it is awkward - these built-in features are designed to work seamlessly with lambdas for operations like filtering, transformation, and counting.
[cgp-00024] useful=140 dangerous=2 :: Prioritize readable, Pythonic options utilizing built-in features over performance-optimized advanced algorithms except the issue explicitly requires optimization or includes large-scale information. A transparent resolution utilizing bin(), str strategies, or listing comprehensions is commonly preferable to bit manipulation or handbook loops. Optimize solely when needed.
[cgp-00047] useful=56 dangerous=2 :: Comply with a correctness-first growth technique: (1) implement the easy algorithm that accurately solves the issue, even when it isn't optimally environment friendly, (2) confirm correctness with check instances, (3) solely then think about optimization if efficiency is insufficient or the issue explicitly requires it. An accurate O(n) resolution is infinitely higher than a buggy O(log n) try. Untimely optimization typically introduces errors in logic, particularly for mathematical or algorithmic issues.
[cgp-00050] useful=0 dangerous=0 :: When a number of algorithmically appropriate options exist, favor the one with higher time/house complexity. An accurate O(1) formula-based resolution is superior to an accurate O(n) iterative resolution. Nonetheless, solely optimize for those who can preserve correctness - a working O(n) resolution is infinitely higher than a buggy O(1) try. Confirm the extra environment friendly method passes all exams earlier than committing to it.
[cgp-00053] useful=0 dangerous=0 :: When implementing mathematical optimizations (particularly for pair/mixture counting), confirm the optimized method in opposition to check instances via handbook calculation BEFORE coding. For every check case: (1) apply your mathematical perception to foretell the output, (2) verify it matches anticipated output, (3) solely then implement. This catches errors in mathematical reasoning early, stopping bugs which can be more durable to debug in code than in arithmetic.
[cgp-00057] useful=0 dangerous=0 :: Keep away from shadowing Python built-in names (dict, listing, str, int, set, tuple, and so on.) when naming variables or parameters. Use descriptive alternate options as a substitute: 'd' or 'information' as a substitute of 'dict', 'lst' or 'objects' as a substitute of 'listing', 's' or 'textual content' as a substitute of 'str'. Shadowing built-ins makes them inaccessible in that scope and reduces code readability, though it is syntactically legitimate.
[cgp-00059] useful=2 dangerous=0 :: Embody defensive programming practices (enter validation, bounds checking, sort checking) even when not explicitly examined by seen check instances. For string indexing, validate index bounds earlier than entry. For numeric conversions, confirm the enter is a sound digit. For listing operations, verify for empty collections. These safeguards improve code robustness and stop runtime errors on edge instances that will exist in hidden exams, demonstrating production-quality coding practices.
[cgp-00074] useful=0 dangerous=0 :: For operations involving powers of two, favor bitwise shift operators over arithmetic operations for readability and effectivity: use left shift (1 << ok) as a substitute of two**ok or pow(2, ok) for computing 2^ok, use proper shift (n >> ok) as a substitute of n // (2**ok) for dividing by powers of two. Bitwise operators make the bit-level intent specific and are the idiomatic method in bit manipulation contexts. That is particularly helpful when working with bit positions and their corresponding values.
[cgp-00081] useful=0 dangerous=0 :: Earlier than utilizing customary library mathematical constants (math.pi, math.e, and so on.), validate that check instances count on full-precision values by calculating one check output and evaluating to anticipated. If anticipated outputs recommend truncated/simplified constants (pi=3.14, pi=3.1415, e=2.718), use hardcoded values matching check precision as a substitute of library constants. Sample: (1) establish mathematical fixed wanted, (2) calculate check output with customary fixed, (3) if mismatch exists, derive the fixed worth that produces actual anticipated outputs, (4) use hardcoded worth. Take a look at case expectations override mathematical purity.
## COMMON PYTHON PATTERNS
[cpp-00010] useful=23 dangerous=0 :: For locating components with most/minimal properties based mostly on a criterion, use built-in max()/min() features with the important thing parameter. Instance: max(list_of_lists, key=len) finds the longest listing. That is extra Pythonic and readable than handbook iteration with comparisons.
[cpp-00013] useful=17 dangerous=0 :: For counting or looking out operations in Python collections (tuples, lists, strings), prioritize built-in strategies: use .depend() for incidence counting, .index() for locating positions, .discover() for strings. These are extra dependable, environment friendly, and Pythonic than handbook iteration with counters or loops.
[cpp-00014] useful=3 dangerous=0 :: When working with mixed-type information buildings, use isinstance() for sort checking to differentiate between completely different aspect sorts. Mix with len() checks to validate construction. Instance: isinstance(merchandise, listing) and len(merchandise) == 2 reliably identifies 2-element lists in combined collections.
[cpp-00015] useful=3 dangerous=0 :: Use prolong() as a substitute of append() when including a number of components from a sequence to an inventory. prolong() provides components individually to the goal listing, whereas append() would add your complete sequence as a single nested aspect. Instance: end result.prolong([value] * depend) vs end result.append([value] * depend).
[cpp-00016] useful=2 dangerous=0 :: Use listing multiplication ([value] * depend) to effectively repeat components. That is extra Pythonic and readable than handbook loops for creating repeated components. Mix with prolong() for including repeated components to current lists.
[cpp-00019] useful=2 dangerous=0 :: For counting components matching a situation with lambda features, use sum(map(lambda x: 1 if situation else 0, iterable)) as a sublime various to len(listing(filter(lambda x: situation, iterable))). The sum(map()) method maps components to 1/0 and sums them, typically extra readable and environment friendly than filtering then counting.
[cpp-00026] useful=14 dangerous=0 :: For changing sequences (tuples, lists) of characters/strings right into a single string, use str.be a part of() methodology: ''.be a part of(sequence) for character concatenation, or 'separator'.be a part of(sequence) for becoming a member of with delimiters. That is the idiomatic Python method - extra readable and performant than handbook loops with += or accumulation patterns.
[cpp-00030] useful=1 dangerous=0 :: For character classification with regex, use re.findall() with mutually unique character class patterns. For 'the whole lot else' classes (like particular characters), favor negation patterns [^...] over enumerating particular characters - e.g., [^A-Za-z0-9] captures all non-alphanumeric characters comprehensively, avoiding the brittleness of lists like [,.!?]. Guarantee patterns do not overlap to stop double-counting.
[cpp-00031] useful=2 dangerous=0 :: For locating international most/minimal throughout nested iterables (listing of tuples, listing of lists, and so on.), use nested generator expressions with built-in max()/min(): `max(aspect for container in containers for aspect in container)`. This sample naturally flattens one stage of nesting with out creating intermediate lists, making it ultimate for locating extremes throughout tuple information or sublists. Extra environment friendly and readable than handbook iteration.
[cpp-00033] useful=2 dangerous=0 :: For index-based entry to dictionary keys, use the sample listing(dict)[index] or listing(dict.keys())[index]. This depends on Python 3.7+ ensures that dictionaries preserve insertion order. Changing the dictionary to an inventory extracts keys so as, permitting customary listing indexing. That is the idiomatic Python resolution for mapping numeric indices to dictionary keys.
[cpp-00036] useful=27 dangerous=2 :: For mathematical operations (GCD, LCM, factorial, prime checking, trigonometry), verify Python's math module FIRST earlier than implementing algorithms manually. Constructed-in features like math.gcd(), math.factorial(), math.isqrt() are well-tested, optimized, and scale back implementation errors. Sample: (1) Perceive the mathematical definition, (2) Test if math module supplies the operation, (3) Use it straight or wrap it with problem-specific logic (e.g., is_coprime = math.gcd(a,b) == 1).
[cpp-00038] useful=0 dangerous=0 :: For checking if a quantity is an ideal sq., use math.isqrt() as a substitute of math.sqrt() to keep away from floating-point precision errors. Sample: b = math.isqrt(n); is_perfect_square = (b * b == n). The isqrt() perform returns the integer sq. root, and squaring it again permits actual integer comparability with out floating-point rounding points.
[cpp-00043] useful=0 dangerous=0 :: For character filtering issues (eradicating/retaining characters based mostly on membership standards), use the set+comprehension+be a part of sample: (1) Convert filter standards right into a set for O(1) lookup (char_set = set(filter_string)), (2) Use listing comprehension or generator expression to filter (char for char in supply if char not in char_set), (3) Use ''.be a part of() to reconstruct the string. This sample is extra Pythonic, readable, and maintainable than handbook index manipulation or character counting approaches, whereas being equally appropriate and environment friendly.
[cpp-00049] useful=0 dangerous=0 :: When returning tuples or lists with combined numeric sorts (integers and floats), use applicable division operators for every part: integer division (//) for entire quantity outcomes, common division (/) for decimal outcomes. Instance: for sum and common, return (n*(n+1)//2, n*(n+1)/2/n) to make sure sum is int and common is float. This prevents sort mismatches in check assertions.
[cpp-00054] useful=0 dangerous=0 :: For digit-by-digit comparability or manipulation issues (digit distance, digit sum variations, and so on.): Use the string conversion sample: (1) Convert integers to strings with str(), (2) Use zfill(max_length) to pad shorter numbers with main zeros for equal size, (3) Use zip() to pair corresponding digit positions, (4) Apply operations on paired digits and mixture outcomes. Instance: str(num1).zfill(size) and str(num2).zfill(size) then zip() for pairing. This handles different-length numbers elegantly and supplies clear positional entry to digits.
[cpp-00056] useful=5 dangerous=0 :: For checking if all/any components in a group fulfill a situation, use Python's built-in all() or any() features with generator expressions. Sample: all(situation for merchandise in iterable) for common quantification (all should fulfill), any(situation for merchandise in iterable) for existential quantification (no less than one satisfies). That is extra Pythonic, readable, and environment friendly than handbook loops with flags. Widespread use instances: all(v == goal for v in dict.values()) for worth uniformity, any(x > threshold for x in listing) for threshold checking, all(isinstance(x, int) for x in assortment) for sort validation.
[cpp-00060] useful=0 dangerous=0 :: For whitespace normalization (collapsing a number of areas/whitespace into single areas), use the split-join sample: ' '.be a part of(s.break up()). The important thing perception: str.break up() with out arguments has particular habits - it splits on ANY whitespace (areas, tabs, newlines) AND mechanically removes empty strings from the end result, naturally collapsing consecutive whitespace. Mixed with ' '.be a part of(), this creates a clear resolution with out regex imports. This sample is extra Pythonic and maintainable than regex alternate options like re.sub(r' +', ' ', s) for easy whitespace normalization duties.
[cpp-00062] useful=0 dangerous=0 :: For advanced quantity operations (polar/rectangular conversion, section calculation, magnitude), use Python's cmath module features as the primary alternative: cmath.polar(z) for conversion to polar kind (returns magnitude and angle), cmath.rect(r, phi) for polar to rectangular, cmath.section(z) for angle extraction. These built-in features deal with edge instances accurately (e.g., treating actual numbers as advanced with imaginary half 0) and are extra dependable than handbook trigonometric calculations. Sample: import cmath → use applicable perform → deal with the return sort (typically tuples).
[cpp-00064] useful=0 dangerous=0 :: For grouping components by a key whereas preserving insertion order (vital for tie-breaking in subsequent sorting), use collections.OrderedDict with setdefault sample: from collections import OrderedDict; grouped = OrderedDict(); for merchandise in objects: grouped.setdefault(key, []).append(worth). Whereas Python 3.7+ dicts preserve insertion order, OrderedDict makes the intent specific and is safer when order issues for downstream operations like sorting by aggregated properties the place equal values ought to preserve authentic encounter order.
[cpp-00065] useful=0 dangerous=0 :: For creating tuples with variable-length unpacked components, use the * unpacking operator: (first, *middle_elements, final) unpacks an inventory/tuple into particular person tuple positions. Instance: (key, *values, depend) the place values is an inventory creates a tuple with key, all values unpacked as separate components, and depend on the finish. That is important when output format requires flattening nested buildings into single-level tuples with variable aspect counts.
[cpp-00069] useful=0 dangerous=0 :: For regex sample matching issues requiring full string matches, select between re.search(), re.match(), and re.fullmatch() based mostly on matching scope: re.match() matches from the beginning, re.search() finds patterns wherever, re.fullmatch() requires your complete string to match. When full string matching is required, both use re.fullmatch() with the sample straight, or use re.search()/re.match() with specific anchors (^ for begin, $ for finish). Instance: re.fullmatch('a.*b', s) is equal to re.search('^a.*b$', s). Each approaches are legitimate - fullmatch() makes the intent specific, whereas search() with anchors supplies extra flexibility. At all times analyze check instances to find out if partial or full string matching is required.
[cpp-00072] useful=1 dangerous=0 :: For counting components in an iterable that match a situation, use the generator expression sample with sum(): sum(1 for x in iterable if situation). This supplies optimum steadiness of readability, reminiscence effectivity, and Pythonic fashion in comparison with alternate options like len([x for x in iterable if condition]) which creates an intermediate listing. For character-level string operations, favor built-in string strategies (isdigit(), isalpha(), isalnum(), isupper(), islower()) over handbook ASCII vary comparisons - they deal with edge instances accurately, enhance readability, and are extra maintainable.
[cpp-00073] useful=0 dangerous=0 :: For bit manipulation issues (discovering set bits, MSB/LSB positions, bit counting), verify Python's integer bit strategies FIRST earlier than implementing handbook algorithms: bit_length() returns the variety of bits wanted to characterize the integer (helpful for MSB place), bit_count() counts set bits (Python 3.10+), as_integer_ratio() for rational illustration. These built-in strategies are optimized, deal with edge instances (together with 0), and infrequently get rid of the necessity for handbook bit-by-bit iteration. Sample: perceive what bit property you want, verify if a built-in methodology supplies it straight.
[cpp-00076] useful=0 dangerous=0 :: For grouping consecutive similar components in a sequence, use itertools.groupby() because the canonical Python resolution. Sample: [list(group) for key, group in itertools.groupby(sequence)]. The groupby perform returns (key, group_iterator) tuples the place secret is the aspect worth and group is an iterator of consecutive occurrences. Convert every group iterator to an inventory to materialize outcomes. Crucial distinction: groupby teams CONSECUTIVE similar components solely - non-consecutive duplicates kind separate teams, making it ultimate for run-length encoding and consecutive duplicate detection with out handbook index monitoring.
## H&LING EDGE CASES
[hec-00021] useful=2 dangerous=0 :: When utilizing mathematical operations like modulo (%), division, or exponentiation, confirm the answer handles unfavorable numbers accurately. For instance, modulo operator works accurately for each constructive and unfavorable integers in Python (e.g., -18 % 2 == 0 for even quantity checking), however habits could differ from expectations in different languages.
## ALGORITHM DESIGN
[ad-00001] useful=1 dangerous=2 :: For recursive GCD issues, use the Euclidean algorithm: base case is b == 0 (return a), recursive case is gcd(b, a % b). This handles all edge instances naturally together with argument ordering, equal numbers, and divisibility.
[ad-00006] useful=0 dangerous=0 :: For bidirectional character swap issues (A↔B) utilizing regex: use re.sub() with a callback perform in a single go. Sample: (1) Create a personality class matching all swap targets (e.g., r'[ _]'), (2) Implement callback that examines every match and returns its counterpart. This avoids ambiguity from sequential replacements the place new characters turn into indistinguishable from originals.
[ad-00008] useful=0 dangerous=0 :: For modular arithmetic issues (nCr mod p, and so on.), verify if p have to be prime. If p could be composite, keep away from algorithms requiring modular inverse (like Fermat's Little Theorem). As a substitute, use approaches that keep away from division totally, resembling Pascal's triangle with DP: C[j] = (C[j] + C[j-1]) % p, which works for ANY modulus.
[ad-00009] useful=0 dangerous=0 :: When division is required in modular arithmetic: (1) If modulus is assured prime, use Fermat's Little Theorem: a/b mod p = a * b^(p-2) mod p. (2) If modulus could also be composite, use Prolonged Euclidean Algorithm for modular inverse, or higher but, redesign to keep away from division (e.g., use recurrence relations like Pascal's triangle).
[ad-00017] useful=1 dangerous=0 :: For decoding issues with combined encoded/non-encoded components: (1) use sort checking to differentiate aspect sorts, (2) validate encoded aspect construction, (3) deal with every sort appropriately in a single go. Prioritize easy iterative approaches with specific conditionals over advanced comprehensions for higher readability and maintainability.
[ad-00018] useful=4 dangerous=0 :: For max sum issues with non-adjacent aspect constraints: Use dynamic programming with recurrence dp[i] = max(arr[i] + dp[i-2], dp[i-1]), representing the selection to incorporate present aspect (add to finest from i-2) or exclude it (preserve finest from i-1). Deal with edge instances: empty array returns 0, single aspect returns that aspect, initialize dp[0] = arr[0] and dp[1] = max(arr[0], arr[1]). Time: O(n), Area: O(n) or O(1) with optimization.
[ad-00023] useful=0 dangerous=0 :: For bit counting and parity checking issues: A number of legitimate approaches exist with completely different trade-offs. (1) Pythonic method: bin(n).depend('1') - most readable and maintainable, (2) Bit manipulation: repeatedly use x & (x-1) to clear lowest set bit - higher efficiency for big inputs, (3) XOR discount for parity. Select the Pythonic method by default except efficiency profiling reveals it is a bottleneck.
[ad-00028] useful=1 dangerous=1 :: For bit toggling issues: (1) Create a masks with 1s at positions to be toggled, (2) Use XOR operation (n ^ masks) to toggle these bits. For variable-length numbers, use bit_length() to find out what number of bits to course of. Instance: to toggle bits at positions 1,3,5 as much as bit_length, generate masks = sum(1 << i for i in vary(1, n.bit_length(), 2)).
[ad-00037] useful=0 dangerous=0 :: For aspect rearrangement/partitioning issues (transfer zeros to finish, separate by situation, and so on.): Use the filter+concatenate sample: (1) filter components into separate teams utilizing listing comprehensions [x for x in lst if condition], (2) depend or accumulate every group individually, (3) concatenate teams in required order. This Pythonic method utilizing built-ins (listing comprehension, depend(), listing multiplication) is commonly clearer and equally appropriate in comparison with in-place two-pointer algorithms, particularly for small to medium datasets.
[ad-00039] useful=0 dangerous=0 :: For 'sum of two squares' issues (checking if n = a² + b²): Use single-loop optimization O(√n) as a substitute of nested loops O(n). Iterate one variable from 0 to √n, calculate the rest (n - a²), and verify if the rest is an ideal sq. utilizing math.isqrt(). Return True instantly upon discovering legitimate pair. This sample: (1) reduces time complexity, (2) handles edge instances naturally (a=0, a=√n), (3) avoids floating-point errors with isqrt().
[ad-00041] useful=4 dangerous=1 :: For geometry and formula-based mathematical issues: Comply with a structured method: (1) Determine the proper mathematical formulation from downside area data, (2) Implement the formulation as a direct translation into code utilizing math module features, (3) Keep away from reimplementing mathematical features or constants that exist in customary libraries, (4) Confirm the formulation with no less than one check case earlier than coding. Direct formulation translation results in cleaner, extra maintainable code with higher numerical precision.
[ad-00042] useful=0 dangerous=0 :: For issues choosing components from each ends of a group (ok smallest AND ok largest), use approaches that deal with overlap: (1) Index-based choice: iterate sorted assortment and embody components the place idx < ok OR idx >= len-k, guaranteeing every aspect chosen as soon as, or (2) Set union: mix subsets with set(min_k + max_k) then type to get rid of duplicates. At all times think about edge instances the place ok*2 >= collection_size, as this ensures overlap between minimal and most choices. Keep away from easy listing concatenation which creates duplicates when ranges overlap.
[ad-00045] useful=0 dangerous=0 :: For 'discover the n-th quantity with property X' issues: Use the iterative counting sample: (1) implement a helper perform to verify if a quantity satisfies the property, (2) iterate via candidate numbers ranging from an applicable preliminary worth, (3) preserve a counter for numbers that fulfill the property, (4) return the candidate when counter reaches n. This sample works for prime numbers, good squares, numbers with particular factorization properties, and so on. It is easy to implement accurately and optimize later if wanted.
[ad-00046] useful=3 dangerous=0 :: For counting distinct prime components: Use the usual factorization sample: (1) iterate potential divisors from 2 to sqrt(n), (2) for every divisor that divides n, increment the distinct issue depend, then divide n by that divisor repeatedly till it not divides (this ensures every prime is counted as soon as no matter its energy), (3) after the loop, if n > 1, it is a remaining prime issue (depend it), (4) optimize by checking divisor 2 individually, then solely odd numbers. This accurately distinguishes between distinct primes and their multiplicities.
[ad-00048] useful=1 dangerous=0 :: For mathematical sequence issues (sum of first n numbers, arithmetic/geometric sequence, factorial-related), verify if a closed-form formulation exists earlier than implementing iterative options. Widespread formulation: sum(1..n) = n*(n+1)/2, sum of arithmetic sequence = n*(first+final)/2, sum of geometric sequence = a*(r^n - 1)/(r-1). Components-based options present O(1) time complexity vs O(n) for loops, are much less error-prone, and reveal mathematical perception. At all times confirm formulation correctness with check instances.
[ad-00051] useful=1 dangerous=0 :: For pair-counting issues (depend pairs satisfying a situation), search for mathematical properties that get rid of the necessity for specific enumeration. Sample: (1) Determine what makes a pair legitimate, (2) Discover mathematical properties characterizing legitimate pairs (e.g., for XOR being odd: one quantity have to be even, different odd), (3) Rework right into a counting downside (depend components in every class), (4) Use combinatorics to compute end result (e.g., odd_count × even_count). This reduces O(n²) pair enumeration to O(n) categorization + O(1) calculation.
[ad-00052] useful=0 dangerous=0 :: For issues involving XOR operations, leverage bit-level properties for optimization: (1) XOR result's odd ⟺ operands have completely different parities (one even, one odd), as a result of parity relies on the least important bit, (2) XOR is commutative and associative, permitting reordering, (3) x ^ x = 0 and x ^ 0 = x, helpful for cancellation patterns. Analyze the particular XOR property related to your downside to search out mathematical shortcuts that keep away from brute power computation.
[ad-00061] useful=0 dangerous=0 :: For iterative mathematical sequence issues (sum/product of first n phrases with particular properties): Use a structured 3-step method: (1) Determine the formulation for producing the k-th aspect (e.g., 2k-1 for odd numbers, 2k for even numbers, k² for squares), (2) Decide the operation to use to every aspect (exponentiation, multiplication, transformation), (3) Combination with applicable perform (sum, product, max). Implement utilizing generator expressions with built-ins: sum(operation(formulation(i)) for i in vary(begin, n+1)). Guarantee vary bounds match the sequence indexing (1-indexed sequences want vary(1, n+1)). This sample supplies readability and correctness for issues the place closed-form formulation do not exist or aren't apparent.
[ad-00066] useful=0 dangerous=0 :: For issues requiring grouping, counting, and sorting by aggregated properties: (1) Group components utilizing dict/OrderedDict with setdefault() or defaultdict, selecting OrderedDict when insertion order impacts tie-breaking in sorting, (2) Type teams utilizing sorted() with key perform based mostly on aggregated metric (e.g., key=lambda x: len(x[1]) for depend), (3) Rework output to match required format utilizing applicable unpacking/restructuring. This sample handles 'group by X, type by depend of Y' issues systematically.
[ad-00068] useful=0 dangerous=0 :: For heap-based 'high ok' issues, confirm OUTPUT ORDERING in opposition to check instances, not simply which components to return. Key distinction: (1) heappop() from a min-heap produces ASCENDING order by the heap key, (2) heapq.nlargest(ok, objects, key=func) produces DESCENDING order by key, (3) heapq.nsmallest(ok, objects, key=func) produces ASCENDING order by key. When implementing heap options, hint via check instances to find out if outcomes must be ordered ascending or descending by frequency/precedence. If ordering is unsuitable, both reverse the ultimate listing or change between nlargest/nsmallest, or use the heappop sample. Take a look at case output ordering is authoritative when the issue description does not explicitly specify.
[ad-00070] useful=0 dangerous=0 :: For 2D grid issues with adjacency or choice constraints (cannot choose adjoining cells/rows/columns): Search for alternatives to scale back dimensionality earlier than making use of DP. If constraints permit choosing at most one aspect per column (or row), pre-compute the optimum alternative for every column/row (e.g., max of two rows in a column), remodeling the issue right into a 1D array. Then apply customary 1D DP patterns (like 'home robber' for non-adjacency). This dimensional discount simplifies state house and makes advanced grid issues tractable utilizing well-known DP templates.
[ad-00071] useful=0 dangerous=0 :: Acknowledge the 'home robber' DP sample as a elementary template relevant past linear arrays: any downside involving choosing non-adjacent components to maximise/reduce a sum can use the recurrence dp[i] = max(worth[i] + dp[i-2], dp[i-1]). This sample seems in: linear arrays with spacing constraints, grid issues (after dimensional discount), tree issues (with parent-child constraints), and sequence optimization. While you see 'maximize sum' + 'cannot choose adjoining', instantly think about this template.
[ad-00075] useful=0 dangerous=0 :: For locating probably the most important bit (MSB) worth or place: Use bit_length() methodology which returns the variety of bits required to characterize an integer. For MSB worth, use the sample: 1 << (n.bit_length() - 1), which leverages the connection that the MSB at place ok (0-indexed from proper) has worth 2^ok. The bit_length() method is cleaner than handbook division loops or string conversion strategies. Deal with edge case: bit_length() returns 0 for n=0, so confirm downside constraints or add specific zero dealing with if wanted.
## TEST CASE INTERPRETATION
[tci-00004] useful=0 dangerous=0 :: A number of appropriate implementations can exist for a similar downside. Give attention to algorithmic correctness verified by passing exams, not on matching a selected reference implementation's fashion or construction.
[tci-00011] useful=123 dangerous=2 :: Extract the anticipated OUTPUT FORMAT from check instances, not simply the logic. Test if the return must be a single worth, tuple, listing, or different construction, and guarantee your resolution matches this actual format.
[tci-00022] useful=0 dangerous=1 :: When analyzing check instances, verify if ALL inputs map to the SAME output worth or construction. If that's the case, the answer could also be trivial - merely return that fixed output straight. Do not overcomplicate with pointless transformations (like listing conversions) when a direct return assertion satisfies all necessities. Instance: if all check instances count on empty tuple output, return () no matter enter complexity.
[tci-00025] useful=5 dangerous=0 :: Earlier than selecting an implementation method, deeply perceive the CORE REQUIREMENT from the issue description and check instances. For instance, 'even parity' means 'even depend of 1-bits', not a selected algorithm. Do not lock into a selected method (like bit manipulation) if easier alternate options (like string counting) fulfill the requirement equally nicely.
[tci-00027] useful=17 dangerous=0 :: When downside descriptions use ambiguous terminology (particularly in bit manipulation: 'even bits', 'odd positions', and so on.), work backward from check instances to find the precise sample. Manually hint via examples of their related illustration (binary for bit issues) to find out the bottom reality interpretation. Take a look at instances are authoritative when terminology is unclear.
[tci-00032] useful=0 dangerous=0 :: When issues ask for 'most/minimal of all information/teams', make clear whether or not it means: (1) international excessive throughout all components, or (2) per-group extremes returned as a group. Take a look at instances reveal the excellence: single worth output signifies international excessive, listing/tuple output suggests per-group evaluation. This interpretation impacts whether or not you flatten the construction or protect grouping.
[tci-00034] useful=0 dangerous=0 :: For dictionary-related issues, fastidiously distinguish from check instances whether or not the anticipated output is: (1) a key (string/int), (2) a worth, (3) a key-value pair (tuple), or (4) a group of any of those. The output sort determines whether or not you want dict.keys(), dict.values(), dict.objects(), or direct indexing into transformed buildings. Take a look at case outputs reveal the precise format required.
[tci-00035] useful=3 dangerous=0 :: When perform names or downside descriptions recommend particular habits (e.g., 'parallelogram_perimeter' implying geometric formulation 2*(a+b)), however check instances produce outputs inconsistent with that expectation, belief the check instances because the authoritative specification. Reverse-engineer the precise formulation by calculating what operation on inputs produces the given outputs, then confirm this derived sample in opposition to ALL check instances earlier than implementing. Take a look at case expectations override semantic meanings and area data.
[tci-00040] useful=0 dangerous=0 :: Take a look at outcomes are the first sign of correctness, not line-by-line comparability with reference implementations. In case your resolution passes all exams with higher time complexity (e.g., O(√n) vs O(n)), it isn't simply appropriate however algorithmically superior. Totally different approaches could be equally or extra legitimate - concentrate on correctness verification via exams, not on matching particular implementation kinds.
[tci-00044] useful=2 dangerous=0 :: When encountering undefined or domain-specific mathematical phrases (like 'good quantity', 'fortunate quantity', and so on.), deal with check instances because the authoritative specification. Systematically analyze check case outputs to reverse-engineer the mathematical definition: (1) study the numerical properties of output values (factorization, divisors, digits, and so on.), (2) search for patterns or widespread traits throughout all outputs, (3) formulate a speculation concerning the defining property, (4) confirm the speculation in opposition to ALL check instances. The check instances encode the whole definition when the issue assertion is ambiguous.
[tci-00055] useful=2 dangerous=0 :: When downside terminology is totally ambiguous or undefined (like 'digit distance' which may have a number of interpretations), systematically hint via EACH check case manually to establish the precise sample: (1) Work via inputs and outputs step-by-step within the related illustration, (2) Formulate a speculation about what operation produces these outputs, (3) Confirm the speculation in opposition to ALL remaining check instances, (4) Implement the sample that satisfies all exams. The test-derived sample is the proper specification, no matter what the terminology would possibly recommend in different contexts.
[tci-00058] useful=0 dangerous=0 :: A number of algorithmically completely different options could be equally legitimate in the event that they fulfill all check instances. When deriving necessities from ambiguous specs, use systematic speculation testing: (1) analyze every check case to know input-output relationships, (2) formulate a speculation concerning the underlying rule, (3) validate the speculation in opposition to ALL check instances, (4) implement the sample that passes all exams. Your resolution is appropriate by definition if it satisfies all check necessities, even when it differs structurally from reference implementations or makes use of a distinct interpretation of ambiguous phrases.
[tci-00063] useful=0 dangerous=0 :: In Python, parentheses alone do not create tuples - distinguish between ('worth') which is only a string 'worth' (parentheses are for grouping/priority), and ('worth',) which is a 1-element tuple (trailing comma required). When analyzing check assertions like assert func()==('Matched!'), acknowledge this expects a plain string, not a tuple. Solely ('Matched!',) with a trailing comma or (a, b) with a number of components create tuples. This syntax nuance is vital for matching anticipated return sorts precisely.
[tci-00067] useful=0 dangerous=0 :: When check instances present advanced output buildings (tuples with variable-length unpacked components, nested aggregations), analyze the EXACT construction earlier than coding: (1) Rely components in output tuples/lists, (2) Determine which components are aggregated vs particular person, (3) Decide if nested buildings are flattened (unpacked) or preserved, (4) Test if ordering inside teams issues. Use this structural evaluation to decide on applicable Python constructs (* unpacking, listing flattening, tuple development patterns) that match the anticipated format exactly.
[tci-00077] useful=0 dangerous=0 :: For counting/aggregation issues involving nested buildings (lists of lists, timber, nested dictionaries), when the issue asks to 'depend components' with out specifying the extent, use check instances to find out the counting scope: (1) Test if check outputs recommend counting solely rapid/top-level kids (e.g., len(outer_list)) vs recursive counting of all nested components, (2) Hint via no less than one check case with nested buildings to see which interpretation produces the anticipated output, (3) The only interpretation (top-level counting) is often appropriate except check instances show in any other case. Instance: 'depend lists in [[1,2], [3], [[4,5]]]' may imply 3 (top-level) or 4 (recursive) - check outputs reveal which is anticipated.
[tci-00078] useful=0 dangerous=0 :: For mathematical issues with infinitely many legitimate options (linear Diophantine equations, modular arithmetic, geometric constructions, and so on.), acknowledge that exams count on ONE PARTICULAR resolution, not simply any mathematically appropriate reply. Work via check instances to establish the choice standards (e.g., smallest non-negative values, particular ordering, canonical kind). When selecting algorithms, favor approaches that naturally produce the anticipated resolution sample (e.g., iterative search from x=0 upward for smallest non-negative x) over refined algorithms (e.g., Prolonged Euclidean Algorithm) that require further adjustment logic to match check expectations. The mathematically elegant resolution is not at all times the proper one for passing exams.
[tci-00079] useful=0 dangerous=0 :: For issues involving mathematical constants (pi, e, sqrt(2), and so on.), confirm that check case anticipated outputs match calculations utilizing customary library constants (math.pi, math.e). Calculate no less than one check case output manually utilizing the usual fixed and examine to the anticipated worth. If there is a mismatch in precision (e.g., your 942.477 vs anticipated 942.45), the check instances seemingly count on a simplified/truncated fixed worth (like pi=3.14 or pi=3.1415) reasonably than full precision. Test reference implementations for hardcoded fixed values and use these actual values to match check expectations, even when they're much less mathematically correct.
## DEBUGGING STRATEGIES
[ds-00005] useful=110 dangerous=2 :: Earlier than producing code, mentally hint via the logic in opposition to check instances to confirm correctness. This helps catch logical errors early and builds confidence within the resolution method.
[ds-00029] useful=0 dangerous=0 :: For bit manipulation issues with unclear place indexing, check a number of interpretations systematically: (1) 0-indexed vs 1-indexed, (2) counting from proper vs left, (3) 'even/odd' referring to place vs bit worth. Work via all check instances manually in binary to validate every speculation earlier than implementing. The interpretation that satisfies all check instances is appropriate.
[ds-00080] useful=0 dangerous=0 :: Throughout reasoning section, manually calculate anticipated outputs for no less than one check case utilizing your proposed method and examine in opposition to the precise anticipated output. For numerical issues, confirm precision matches precisely - discrepancies like 942.477 vs 942.45 point out fixed precision mismatches (e.g., utilizing math.pi as a substitute of a truncated worth). This early validation catches precision points, unsuitable formulation, and fixed worth issues earlier than code technology.

These outcomes present that ACE can considerably enhance efficiency on advanced duties like code technology.

Abstract

On this article, we’ve explored quite a bit about context engineering and the ACE method, so let’s briefly recap the important thing takeaways:

• Context engineering has emerged as a vital area as a result of it permits us to enhance LLM efficiency with out prolonged and expensive fine-tuning.
• ACE (Agentic Context Engineering) is without doubt one of the newest approaches to immediate optimisation, leveraging detailed playbooks with atomised bullet factors that embody each directions and metadata.
• As our examples confirmed, immediate optimisation is just not a silver bullet. It doesn’t enhance efficiency in each case. In response to the authors, ACE is only for agentic workflows or extremely specialised domains. In our experiments, it made a transparent distinction in code technology, however had restricted impression on banking intent classification.

The principle takeaway for me is that immediate optimisation received’t clear up your process mechanically. You continue to want a holistic understanding of what info the LLM and brokers have in the course of the optimisation course of and the way finest to construction and refine it. Context issues, and considerate engineering of that context is what makes approaches like ACE efficient.

Thanks for studying. I hope this text was insightful. Bear in mind Einstein’s recommendation: “The necessary factor is to not cease questioning. Curiosity has its personal cause for current.” Might your curiosity lead you to your subsequent nice perception.

Reference

This text was based mostly on the paper and analysis by Zhang et al., revealed in 2025, “Agentic Context Engineering: Evolving Contexts for Self-Enhancing Language Fashions”.