Self-Querying Retrieval: Enhancing RAG search with intelligent filtering
Self-QueryingRetrieval enhances RAG systems by enabling LLMs to distinguish structured filters (dates, categories) from semantic search, rather than simple keyword-based vector retrieval.
The approach first analyzes user queries, separates semantic meaning from metadata, then applies structured filters to refine results more accurately.
This technique addresses a core RAG limitation: the ability to correctly interpret implicit criteria like date ranges or categories in natural language queries.