Unlocking the Power of AI with Chunking
Smart people are lazy. They find the most efficient ways to solve complex problems, minimizing effort while maximizing results.
In Generative AI applications, this efficiency is achieved through chunking. Just like 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 understand the broader framework in which this technique operates: Retrieval-Augmented Generation or RAG.
Unraveling the Mysteries of 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.
Enhancing AI Efficiency with Chunking
