Advanced RAG Techniques

While standard Retrieval Augmented Generation (RAG) is powerful, advanced techniques can significantly enhance its performance, leading to more accurate and contextually aware responses. This article explores several advanced strategies to take your RAG systems to the next level.

Hybrid search combines keyword-based (lexical) search with vector-based (semantic) search. This approach leverages the strengths of both methods, improving retrieval accuracy for queries with specific keywords or jargon.

A re-ranking model can be applied to the initial set of retrieved documents to re-order them based on relevance to the query. This adds a layer of refinement, ensuring the most relevant context is passed to the language model.

Self-correction involves using the LLM to refine the user's query or evaluate the retrieved context. The model can identify ambiguities in the initial query and generate a more specific one, or it can assess whether the retrieved documents are sufficient to answer the question before generating a final response.

By implementing advanced techniques like hybrid search, re-ranking, and self-correction, you can build more sophisticated and reliable RAG systems that deliver superior performance.