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Home Machine Learning

The Artwork of Chunking: Boosting AI Efficiency in RAG Architectures | by Han HELOIR, Ph.D. ☕️ | Aug, 2024

Admin by Admin
August 19, 2024
in Machine Learning
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The Key to Efficient AI-Pushed Retrieval

Han HELOIR, Ph.D. ☕️

Towards Data Science

13 min learn

·

19 hours in the past

Free hyperlink: Please assist me like this LinkedIn submit.

Good individuals are lazy. They discover essentially the most environment friendly methods to unravel complicated issues, minimizing effort whereas maximizing outcomes.

In Generative AI functions, this effectivity is achieved by chunking. Similar to breaking a e-book into chapters makes it simpler to learn, chunking divides vital texts into smaller, manageable elements, making them simpler to course of and perceive.

Earlier than exploring the mechanics of chunking, it’s important to grasp the broader framework wherein this method operates: Retrieval-Augmented Era or RAG.

What’s RAG?

What’s Retrieval Augmented Era

Retrieval-augmented era (RAG) is an method that integrates retrieval mechanisms with massive language fashions (LLM fashions). It enhances AI capabilities utilizing retrieved paperwork to generate extra correct and contextually enriched responses.

Introducing Chunking

What’s chunking

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A Multi-Agent SQL Assistant You Can Belief with Human-in-Loop Checkpoint & LLM Value Management

What PyTorch Actually Means by a Leaf Tensor and Its Grad


The Key to Efficient AI-Pushed Retrieval

Han HELOIR, Ph.D. ☕️

Towards Data Science

13 min learn

·

19 hours in the past

Free hyperlink: Please assist me like this LinkedIn submit.

Good individuals are lazy. They discover essentially the most environment friendly methods to unravel complicated issues, minimizing effort whereas maximizing outcomes.

In Generative AI functions, this effectivity is achieved by chunking. Similar to breaking a e-book into chapters makes it simpler to learn, chunking divides vital texts into smaller, manageable elements, making them simpler to course of and perceive.

Earlier than exploring the mechanics of chunking, it’s important to grasp the broader framework wherein this method operates: Retrieval-Augmented Era or RAG.

What’s RAG?

What’s Retrieval Augmented Era

Retrieval-augmented era (RAG) is an method that integrates retrieval mechanisms with massive language fashions (LLM fashions). It enhances AI capabilities utilizing retrieved paperwork to generate extra correct and contextually enriched responses.

Introducing Chunking

What’s chunking
Tags: ArchitecturesArtAugboostingChunkingHanHELOIRperformancePh.DRAG

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