The Evolution of RAG: Beyond Basic Retrieval

The evolution of RAG marks a decisive step in the maturity of AI systems. Beyond simple vector search, modern techniques combine multiple complementary approaches to improve the relevance and quality of generated responses.

Hybrid search is one of the pillars of this evolution. By combining dense semantic search and sparse lexical search (BM25), systems get the best of both worlds: deep understanding of query meaning coupled with the precision of exact matches.

Recursive summarization represents another major advance. Rather than processing entire documents, this technique decomposes information into hierarchical layers, enabling retrieval at different levels of granularity depending on the nature of the query.

Finally, contextual reranking and result fusion techniques (Reciprocal Rank Fusion) allow refining the relevance of retrieved documents before injecting them into the LLM, significantly improving the final quality of generated answers.