RAG Reranking: How to Improve Retrieval Accuracy in AI Applications

Reranking adds a precise second stage to RAG retrieval. A fast first stage (vector, keyword or hybrid search) gathers a broad candidate set, perhaps the top 50 passages; a reranker, usually a cross-encoder that reads the query and each passage together, re-scores them; and only the top few go to the language model. This often fixes cases where the right passage was retrieved but ranked too low. Tune the candidate count against latency, consider score thresholds for refusals and measure the gain on your own question set.

Reranking follows hybrid search in the RAG pipeline. Its gains depend on good chunking, since a reranker cannot fix a chunk that lacks the answer.

Embedding search compares a query vector with passage vectors computed independently, which is fast but approximate. Keyword search matches terms but not intent. Both produce reasonable candidate sets with imperfect order. Because the language model only sees the top few passages, a relevant passage at rank 15 might as well not exist. Reranking reorders the candidates using a model that reads query and passage together.

The candidate set must contain the right passage for reranking to help, so measure first-stage recall at different sizes. Larger sets improve the chance but add latency and cost. After reranking, choose how many passages to pass on: too few risks missing context, too many adds noise and tokens. Calibrated score thresholds can trigger 'I could not find this in our documents' rather than a weak answer.

Relevance is not the only factor. A newer policy should outrank an older version; an official handbook should outrank a chat message. Combine reranker scores with metadata such as date, document status and source authority, or filter outdated documents before reranking.

Use the same question set as for retrieval evaluation. Compare, with and without reranking: the rank of the correct passage, recall at the number of passages you send to the model, answer faithfulness and correctness, and latency and cost per query. Keep reranking only if gains are clear on your data.

Apply permission and metadata filters before reranking, in the first-stage retrieval. Reranking unfiltered candidates and filtering afterwards wastes compute and risks exposing restricted content in logs or debug views. If filters remove most candidates, increase the first-stage candidate count for filtered queries rather than reranking fewer results. Keep the filter logic in the retrieval service so every application using it inherits the same rules; see enterprise RAG architecture.

Rerankers are available as hosted APIs from several model providers, as open-source cross-encoder models you can run on your own infrastructure, and built into some search engines and vector databases. Hosted APIs are fastest to adopt; self-hosting gives data control and predictable cost at volume but requires GPU or optimized CPU serving. Whichever you choose, check language support for your content and measure latency at your candidate set size.

An illustrative scenario, not a client case: a software company's documentation assistant retrieves the right troubleshooting page in its top 20 for most questions, but often not in the top 3 that reach the model. Adding a cross-encoder reranker over the top 40 candidates moves the correct page into the top 3 much more often in evaluation, with acceptable added latency.

Reranking separates finding candidates from ordering them, which often fixes the gap between 'retrieved somewhere' and 'used in the answer'. Measure recall, tune candidates and keep it only where it helps. Related: hybrid search and RAG guide.