The key to an effective ai-powered recovery
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Intelligent people are lazy. They find the most efficient ways to solve complex problems, minimizing effort and maximizing results.
In generative ai applications, this efficiency is achieved through chunking. Just as dividing a book into chapters makes it easier to read, chunking breaks important texts into smaller, more manageable pieces, making them easier to process and understand.
Before exploring the mechanics of fragmentation, it is essential to understand the broader framework in which this technique operates: Recovery-Augmented Generation or RAG.
What is RAG?
Retrieval Augmented Generation (RAG) is an approach that integrates retrieval mechanisms with large language models (LLMs). It enhances ai capabilities by using retrieved documents to generate more accurate and contextually enriched responses.
