Understanding Multi-Stage Retrieval in RAG Systems

Multi-stage retrieval is a strategy used in Retrieval-Augmented Generation (RAG) to improve the accuracy and relevance of information fetched for language models. Instead of relying on a single retrieval method, it combines two or more stages, typically starting with a fast, broad retrieval (e.g., sparse retrieval like BM25) to get a large set of candidate documents, followed by a more precise, computationally intensive method (e.g., dense retrieval or re-ranking) to narrow down to the most relevant results. This approach balances recall and precision, ensuring the LLM receives high-quality context without overwhelming it with irrelevant data.