DISCLAIMER: The concept of doing Cowl Letter and even Resume with AI doesn’t clearly begin with me. Lots of people have carried out this earlier than (very efficiently) and have constructed web sites and even corporations from the concept. That is only a tutorial on the right way to construct your individual Cowl Letter AI Generator App utilizing Python and some strains of code. All of the code you’ll say on this weblog publish might be present in my public Github folder. Take pleasure in. 🙂
Pep Guardiola is a (very profitable) Manchester Metropolis soccer coach. Throughout Barcelona’s Leo Messi years, he invented a method of enjoying soccer often known as “Tiki-Taka”. Which means as quickly as you obtain the ball, you cross the ball, instantly, with out even controlling it. You possibly can cross the ball 30–40 occasions earlier than scoring a purpose.
Greater than a decade later, we will see how the best way of enjoying soccer made Guardiola and his Barcelona well-known is gone. For those who have a look at a Manchester Metropolis match, they take the ball and instantly search for the striker or the winger. You solely want just a few, vertical passes, instantly searching for the chance. It’s extra predictable, however you do it so many occasions that you’ll finally discover the area to hit the goal.
I feel that the job market has one way or the other gone in the identical route.
Earlier than you had the chance to go to the corporate, hand in your resume, speak to them, be round them, schedule an interview, and actively speak to individuals. You’ll spend weeks getting ready for that journey, sharpening your resume, and reviewing questions and solutions.
For a lot of, this old style technique nonetheless works, and I imagine it. When you have a very good networking alternative, or the fitting time and place, the handing the resume factor works very properly. We love the human connection, and it is vitally efficient to really know somebody.
It is very important contemplate that there’s a complete different strategy as properly. Corporations like LinkedIn, Certainly, and even basically the web utterly modified the sport. You possibly can ship so many resumes to so many corporations and discover a job out of statistics. AI is altering this recreation a bit of bit additional. There are numerous AI instruments to tailor your resume for the precise firm, make your resume extra spectacular, or construct the job particular cowl letter. There are certainly many corporations that promote this type of companies to individuals which are searching for jobs.
Now, imagine me, I’ve received nothing in opposition to these corporations, in any respect, however the AI that they’re utilizing it’s not likely “their AI”. What I imply by that’s that when you use ChatGPT, Gemini, or the tremendous new DeepSeek to do the precise job you’ll very doubtless not get a worse response than the (paid) instrument that you’re utilizing on their web site. You might be actually paying for the “commodity” of getting a backend API that does what we must do by ChatGPT. And that’s honest.
Nonetheless, I wish to present you that it’s certainly quite simple and low cost to make your individual “resume assistant” utilizing Massive Language Fashions. Specifically, I wish to give attention to cowl letters. You give me your resume and the job description, and I offer you your cowl letter you’ll be able to copy and paste to LinkedIn, Certainly, or your e-mail.
In a single picture, it can seem like this:
Now, Massive Language Fashions (LLMs) are particular AI fashions that produce textual content. Extra particularly, they’re HUGE Machine Studying fashions (even the small ones are very large).
Which means constructing your individual LLM or coaching one from scratch could be very, very costly. We received’t do something like that. We’ll use a wonderfully working LLM and we are going to neatly instruct it to carry out our job. Extra particularly, we are going to do this in Python and utilizing some APIs. Formally, it’s a paid API. Nonetheless, since I began the entire challenge (with all of the trial and error course of) I spent lower than 30 cents. You’ll doubtless spend 4 or 5 cents on it.
Moreover, we are going to make a working net app that may assist you to have your cowl letter in just a few clicks. It is going to be an effort of a pair hundred strains of code (with areas 🙂).
To encourage you, listed here are screenshots of the ultimate app:
Fairly cool proper? It took me lower than 5 hours to construct the entire thing from scratch. Consider me: it’s that straightforward. On this weblog publish, we are going to describe, so as:
- The LLM API Technique. This half will assist the reader perceive what LLM Brokers we’re utilizing and the way we’re connecting them.
- The LLM Object. That is the implementation of the LLM API technique above utilizing Python.
- The Internet App and outcomes. The LLM Object is then transferred into an internet app utilizing Streamlit. I’ll present you the right way to entry it and a few outcomes.
I’ll attempt to be as particular as potential so that you’ve got every little thing it’s good to make it your self, but when these things will get too technical, be happy to skip to half 3 and simply benefit from the sundown 🙃.
Let’s get began!
1. LLM API Technique
That is the Machine Studying System Design a part of this challenge, which I stored extraordinarily gentle, as a result of I needed to maximise the readability of the entire strategy (and since it truthfully didn’t should be extra sophisticated than that).
We’ll use two APIs:
- A Doc Parser LLM API will learn the Resume and extract all of the significant info. This info will probably be put in a .json file in order that, in manufacturing, we could have the resume already processed and saved someplace in our reminiscence.
- A canopy letter LLM API. This API will learn the parsed resume (the output of the earlier API) and the job description and it’ll output the Cowl Letter.
Two details:
- What’s the finest LLM for this job? For textual content extraction and summarization, LLama or Gemma are recognized to be a fairly low cost and environment friendly LLM. As we’re going to use LLama for the summarization job, with the intention to hold consistency, we will undertake it for the opposite API as properly. If you wish to use one other mannequin, be happy to take action.
- How will we join the APIs? There are a selection of the way you are able to do that. I made a decision to present it a attempt to Llama API. The documentation will not be precisely in depth, nevertheless it works properly and it means that you can play with many fashions. You’ll need to log in, purchase some credit score ($1 is greater than enough for this job), and save your API key. Be at liberty to modify to a different resolution (like Hugging Face or Langchain) when you really feel prefer it.
Okay, now that we all know what to do, we simply want to really implement it in Python.
2. LLM Object
The very first thing that we want is the precise LLM prompts. Within the API, the prompts are normally handed utilizing a dictionary. As they are often fairly lengthy, and their construction is all the time comparable, it is smart to retailer them in .json recordsdata. We’ll learn the JSON recordsdata and use them as inputs for the API name.
2.1 LLM Prompts
On this .json file, you’ll have the mannequin (you’ll be able to name no matter mannequin you want) and the content material which is the instruction for the LLM. In fact, the content material key has a static half, which is the “instruction” and a “dynamic” half, which is the precise enter of the API name. For instance: that is the .json file for the primary API, I known as it resume_parser_api.json:
As you’ll be able to see from the “content material” there may be the static name:
“You’re a resume parser. You’ll extract info from this resume and put them in a .json file. The keys of your dictionary will probably be first_name, last_name, location, work_experience, school_experience, expertise. In deciding on the data, hold observe of essentially the most insightful.”
The keys I wish to extract out of my “.json” recordsdata are:
[first_name, last_name, location, work_experience, school_experience, skills]Be at liberty so as to add something extra info that you just wish to be “extracted” out of your resume, however do not forget that that is stuff that ought to matter solely in your cowl letter. The particular resume will probably be added after this textual content to kind the total name/instruction. Extra on that later.
The order instruction is the cover_letter_api.json:
Now the instruction is that this one:
“You might be an professional in job looking and a canopy letter author. Given a resume json file, the job description, and the date, write a canopy letter for this candidate. Be persuasive {and professional}. Resume JSON: {resume_json} ; Job Description: {job_description}, Date: {date}”
As you’ll be able to see, there are three placeholders: “Resume_json”, “job_description” and “date”. As earlier than, these placeholders will then get replaced with the right info to kind the total immediate.
2.2 constants.py
I made a really small constants.py file with the trail of the 2 .json immediate recordsdata and the API that it’s a must to generate from LLamaApi (or actually no matter API you might be utilizing). Modify this if you wish to run the file domestically.
2.3 file_loader.py
This file is a set of “loaders” in your resume. Boring stuff however vital.
2.4 cover_letter.py
The entire implementation of the LLM Technique might be discovered on this object that I known as CoverLetterAI. There it’s:
I spent fairly a while attempting to make every little thing modular and simple to learn. I additionally made numerous feedback to all of the capabilities so you’ll be able to see precisely what does what. How will we use this beast?
So the entire code runs in 5 easy strains. Like this:
from cover_letter import CoverLetterAI
cover_letter_AI = CoverLetterAI()
cover_letter_AI.read_candidate_data('path_to_your_resume_file')
cover_letter_AI.profile_candidate()
cover_letter_AI.add_job_description('Insert job description')
cover_letter_AI.write_cover_letter()So so as:
- You name the CoverLetterAI object. It is going to be the star of the present
- You give me the trail to your resume. It may be PDF or Phrase and I learn your info and retailer them in a variable.
- You name profile_candidate(), and I run my first LLM. This course of the candidate phrase data and creates the .json file we are going to use for the second LLM
- You give me the job_description and also you add it to the system. Saved.
- You name write_cover_letter() and I run my second LLM that generates, given the job description and the resume .json file, the quilt letter
3. Internet App and Outcomes
So that’s actually it. You noticed all of the technical particulars of this weblog publish within the earlier paragraphs.
Simply to be further fancy and present you that it really works, I additionally made it an internet app, the place you’ll be able to simply add your resume, add your job description and click on generate cowl letter. That is the hyperlink and that is the code.
Now, the cowl letters which are generated are scary good.
This can be a random one:
February 1, 2025
Hiring Supervisor,
[Company I am intentionally blurring]I’m thrilled to use for the Distinguished AI Engineer place at [Company I am intentionally blurring], the place I can leverage my ardour for constructing accountable and scalable AI programs to revolutionize the banking trade. As a seasoned machine studying engineer and researcher with a robust background in physics and engineering, I’m assured that my expertise and expertise align with the necessities of this position.
With a Ph.D. in Aerospace Engineering and Engineering Mechanics from the College of Cincinnati and a Grasp’s diploma in Physics of Complicated Methods and Huge Knowledge from the College of Rome Tor Vergata, I possess a novel mix of theoretical and sensible data. My expertise in growing and deploying AI fashions, designing and implementing machine studying algorithms, and dealing with giant datasets has geared up me with the abilities to drive innovation in AI engineering.
As a Analysis and Educating Assistant on the College of Cincinnati, I utilized surrogate fashions to detect and classify cracks in pipes, reaching a 14% enchancment in injury detection experiments. I additionally developed surrogate fashions utilizing deep studying algorithms to speed up Finite Ingredient Strategies (FEM) simulations, leading to a 1M-fold discount in computational time. My expertise in educating and creating programs in sign processing and picture processing for teenagers curious about AI has honed my capacity to speak advanced ideas successfully.
In my earlier roles as a Machine Studying Engineer at Gen 9, Inc., Apex Microdevices, and Accenture, I’ve efficiently designed, developed, and deployed AI-powered options, together with configuring mmWave radar and Jetson gadgets for knowledge assortment, implementing state-of-the-art level cloud algorithms, and main the FastMRI challenge to speed up MRI scan occasions. My experience in programming languages corresponding to Python, TensorFlow, PyTorch, and MATLAB, in addition to my expertise with cloud platforms like AWS, Docker, and Kubernetes, has enabled me to develop and deploy scalable AI options.
I’m significantly drawn to [Company I am intentionally blurring] dedication to creating accountable and dependable AI programs that prioritize buyer expertise and ease. My ardour for staying abreast of the newest AI analysis and my capacity to judiciously apply novel strategies in manufacturing align with the corporate’s imaginative and prescient. I’m excited in regards to the alternative to work with a cross-functional staff of engineers, analysis scientists, and product managers to ship AI-powered merchandise that remodel how [Company I am intentionally blurring] serves its prospects.
Along with my technical expertise and expertise, I possess glorious communication and presentation expertise, which have been demonstrated by my technical writing expertise at In direction of Knowledge Science, the place I’ve written complete articles on machine studying and knowledge science, reaching a broad viewers of 50k+ month-to-month viewers.
Thanks for contemplating my utility. I’m keen to debate how my expertise and expertise can contribute to the success of the [Company I am intentionally blurring] and [Company I am intentionally blurring]’s mission to convey humanity and ease to banking by AI. I’m assured that my ardour for AI, my technical experience, and my capacity to work collaboratively will make me a priceless asset to your staff.
Sincerely,
Piero Paialunga
They give the impression of being similar to I might write them for a particular job description. That being stated, in 2025, it’s good to watch out as a result of hiring managers do know that you’re utilizing AI to write down them and the “laptop tone” is fairly simple to identify (e.g. phrases like “keen” are very ChatGPT-ish lol). Because of this, I’d wish to say to use these instruments properly. Positive, you’ll be able to construct your “template” with them, however make sure to add your private contact, in any other case your cowl letter will probably be precisely like the opposite hundreds of canopy letters that the opposite candidates are sending in.
That is the code to construct the online app.
4. Conclusions
On this weblog article, we found the right way to use LLM to transform your resume and job description into a particular cowl letter. These are the factors we touched:
- Using AI in job looking. Within the first chapter we mentioned how job looking is now utterly revolutionized by AI.
- Massive Language Fashions concept. It is very important design the LLM APIs properly. We did that within the second paragraph
- LLM API implementation. We used Python to implement the LLM APIs organically and effectively
- The Internet App. We used streamlit to construct a Internet App API to show the facility of this strategy.
- Limits of this strategy. I feel that AI generated cowl letters are certainly superb. They’re on level, skilled and properly crafted. Nonetheless, if everybody begins utilizing AI to construct cowl letters, all of them actually look the identical, or not less than all of them have the identical tone, which isn’t nice. One thing to consider.
5. References and different good implementations
I really feel that’s simply honest to say numerous good those who have had this concept earlier than me and have made this public and out there for anybody. That is only some of them I discovered on-line.
Cowl Letter Craft by Balaji Kesavan is a Streamlit app that implements a really comparable concept of crafting the quilt letter utilizing AI. What we do totally different from that app is that we extract the resume immediately from the phrase or PDF, whereas his app requires copy-pasteing. That being stated, I feel the man is extremely gifted and really inventive and I like to recommend giving a glance to his portoflio.
Randy Pettus has a comparable concept as properly. The distinction between his strategy and the one proposed on this tutorial is that he’s very particular within the info, asking questions like “present hiring supervisor” and the temperature of the mannequin. It’s very fascinating (and good) which you could clearly see the best way he’s pondering of Cowl Letters to information the AI to construct it the best way he likes them. Extremely really helpful.
Juan Esteban Cepeda does an excellent job in his app as properly. You too can inform that he was engaged on making it greater than a easy streamlit add as a result of he added the hyperlink to his firm and a bunch of evaluations by customers. Nice job and nice hustle. 🙂
6. About me!
Thanks once more in your time. It means quite a bit ❤
My identify is Piero Paialunga and I’m this man right here:
I’m a Ph.D. candidate on the College of Cincinnati Aerospace Engineering Division and a Machine Studying Engineer for Gen 9. I speak about AI, and Machine Studying in my weblog posts and on Linkedin. For those who favored the article and wish to know extra about machine studying and comply with my research you’ll be able to:
A. Observe me on Linkedin, the place I publish all my tales
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C. Turn out to be a referred member, so that you received’t have any “most variety of tales for the month” and you’ll learn no matter I (and hundreds of different Machine Studying and Knowledge Science high writers) write in regards to the latest expertise out there.
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If you wish to ask me questions or begin a collaboration, depart a message right here or on Linkedin:
