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Smart persons are lazy. They find probably the most efficient ways to resolve complex problems, minimizing effort while maximizing results.
In Generative AI applications, this efficiency is achieved through chunking. Similar to breaking a book into chapters makes it easier to read, chunking divides significant texts into smaller, manageable parts, making them easier to process and understand.
Before exploring the mechanics of chunking, it’s essential to grasp the broader framework by which this system operates: Retrieval-Augmented Generation or RAG.
What’s RAG?
Retrieval-augmented generation (RAG) is an approach that integrates retrieval mechanisms with large language models (LLM models). It enhances AI capabilities using retrieved documents to generate more accurate and contextually enriched responses.
Introducing Chunking
