Re-ranking

Re-ranking is a two-stage retrieval approach where an initial set of candidate results (retrieved using fast methods like vector search or keyword search) is reordered by a more sophisticated model that evaluates each result's relevance to the query more carefully. The initial retrieval stage prioritises recall — finding all potentially relevant documents quickly. The re-ranking stage prioritises precision — determining which of those candidates are most relevant. This two-stage approach balances speed (fast initial retrieval from large collections) with quality (accurate relevance scoring on a smaller set). Re-ranking models are typically cross-encoders — models that jointly process the query and each candidate document together, rather than comparing pre-computed embeddings. This joint processing allows the model to capture fine-grained interactions between the query and document that bi-encoder (embedding-based) approaches miss.

Re-ranking is one of the most effective ways to improve retrieval quality in RAG and search systems. Studies show that adding a re-ranking step can improve retrieval precision by 10-30% compared to vector search alone, with a corresponding improvement in downstream AI response quality. For business applications where accuracy matters — legal research, compliance, medical information, financial analysis — the improvement from re-ranking can be the difference between a useful and an unreliable system. The cost of re-ranking (typically a few milliseconds per query) is negligible compared to the value of better results. Re-ranking also helps when dealing with diverse document types and query patterns. While a single embedding model may not handle all content types equally well, a cross-encoder re-ranker can compensate by providing a more nuanced relevance assessment that considers the specific relationship between each query and document.