Reranking

Reranking separates finding plausible information from deciding which information best answers a query. A first-stage retriever searches a large collection using keyword matching, vector similarity, or a hybrid of both. A second stage scores the retrieved candidates using richer relevance signals and changes their order. The application then selects the highest-ranked results for a search interface or for the context supplied to a foundation model in a retrieval-augmented generation, or RAG, workflow.

This two-stage design balances retrieval coverage with computational expense. Embedding-based retrieval typically compares a query vector against document vectors calculated during ingestion. A common neural reranker instead uses a cross-encoder that processes the query and each candidate together, allowing closer evaluation of their relationship. Cross-encoders are common implementations, not the definition of reranking: rules, learned ranking models, and other model architectures can also reorder candidates.

For example, an operations assistant receives the question, “How do I rotate production database credentials without downtime?” Hybrid search retrieves 50 passages, combining exact matches for credential terminology with semantically related rotation procedures. A reranker promotes passages describing overlapping credential validity and application connection refresh, while demoting general password policy documents. The application supplies the best five passages to its generation model, subject to its context budget. These candidate and output counts are illustrative tuning choices, not universal defaults.

In a cloud architecture, a managed search service can perform initial retrieval, while a managed reranking API or a custom model endpoint performs second-stage scoring. Apply tenant and document authorization filters before unauthorized content reaches the reranker or generation model. Preserve document identifiers and source metadata through scoring so the final answer can cite its evidence. Treat retrieved text as untrusted input; a relevance score does not establish factual accuracy, authorization, or safety.

Candidate count, passage length, model choice, and final context size affect latency, throughput, and cost. Retrieving more candidates can improve coverage, but scoring them requires additional inference work. Long passages may be truncated according to model limits, potentially removing the relevant evidence. Use bounded candidate sets, appropriate chunking, request timeouts, and an explicit fallback policy, such as using the initial ranking when reranking is unavailable. For custom endpoints, benchmark hardware utilization, batching, concurrency, and autoscaling under realistic traffic.

Evaluate retrieval and reranking separately. Recall at the candidate cutoff helps determine whether the retriever finds the required evidence; metrics such as normalized discounted cumulative gain and mean reciprocal rank measure ranking quality. Also measure answer correctness, citation support, end-to-end latency, and cost. Use representative queries, relevance judgments, and held-out evaluation data. Tune embeddings, chunk boundaries, hybrid retrieval settings, candidate counts, and reranker selection as distinct components rather than changing everything simultaneously.

Reranking is not embedding-model fine-tuning, although both can improve a retrieval pipeline. Improving embeddings can bring previously missed evidence into the candidate set; reranking can only reorder candidates already supplied to it. Hybrid score fusion combines retrieval signals, whereas reranking adds another relevance assessment after candidate retrieval. Reranking also does not guarantee hallucination-free answers: the corpus may lack the answer, the selected passages may be incomplete, or the generation model may fail to follow the evidence.