Advanced RAG Techniques

Retrieval-Augmented Generation (RAG) is a technique used in AI applications that involves integrating a comprehensive knowledge base into a retrieval system to enhance language model generation capabilities. This post explores techniques for improving every part of the RAG pipeline, including indexing, retrieval, and generation. Indexing methods discussed include simple chunking, semantic chunking, and language model-based chunking. Retrieval enhancement strategies involve hybrid search, query rewriting, and fine-tuning embedding models. Finally, generation improvements focus on autocut to remove irrelevant information, reranking retrieved objects, and fine-tuning the LLM on domain-specific data.

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