What is Reranking in Search and RAG?
Reranking is the second-stage scoring of candidate documents in a retrieval pipeline: using a more accurate but slower model (typically a cross-encoder) to reorder the top 50-200 candidates from initial retrieval into a precisely-ranked list of 5-10 documents that go to the LLM or user.
Overview
Reranking is one of the highest-ROI additions to a production RAG or search pipeline. Initial retrieval (semantic ANN, keyword BM25, or hybrid) returns documents that are roughly relevant. Reranking applies a second, more careful model to score the top 50-200 candidates by true relevance to the query: surfacing the actually-best matches from a pile of plausible candidates. The standard pattern in production: retrieve 100, rerank to 10, send 5-10 to the LLM. Cohere Rerank, BGE-reranker, ColBERT, and increasingly LLM-as-reranker patterns dominate the production landscape. Adding a reranker typically lifts retrieval quality 10-30% on benchmarks and is one of the cheapest improvements to ship.
Why reranking helps
Initial retrieval models (bi-encoders for semantic search, BM25 for keyword) compute query and document representations independently, then compare them. This is fast and scalable but imprecise: the model never sees query and document together to compute fine-grained relevance. Cross-encoder rerankers process query and document together, computing relevance via attention across the joint pair. Much more accurate, much slower per pair (50-200ms vs sub-millisecond for ANN). The two-stage pipeline gets the best of both: ANN's scalability for finding rough candidates, cross-encoder's accuracy for picking the actual best from those candidates. Without reranking, top retrieved documents are often only ~60-70% precision; with reranking, top documents reach 85-95% precision on most benchmarks.
Production reranking options
(1) Cohere Rerank: managed API, very strong out-of-box, supports 100+ languages, costs ~$0.001-0.002 per query at typical workloads; widely used in production. (2) BGE-reranker (open source): strong quality, can be self-hosted on a single GPU; good fit for clients with self-hosted requirements. (3) ColBERT: research-grade late-interaction reranker, more setup complexity; powerful when implemented correctly. (4) LLM-as-reranker: using GPT-4 or Claude to score query-document pairs via prompting; highest accuracy, highest cost, slowest; reserved for high-stakes use cases. (5) Cross-encoder fine-tuning: train a custom reranker on your domain data; valuable for specialized domains where off-the-shelf rerankers underperform.
Where reranking sits in the pipeline
Standard production RAG pipeline: (1) Query understanding, possibly rewrite the query for better retrieval (HyDE, multi-query expansion); (2) Initial retrieval: semantic + keyword hybrid, returns top 50-200 candidates; (3) Metadata filtering: apply user permissions, document type filters, recency filters; (4) Reranking: cross-encoder scores filtered candidates, reorders by true relevance, returns top 5-15; (5) Context assembly: format reranked documents into the LLM prompt with citation markers; (6) Generation: LLM produces answer grounded in retrieved documents. Reranking sits at the precision-critical point right before context goes to the LLM.
Use cases
- Improving RAG answer quality by ensuring top retrieved documents are actually relevant
- Search results pages where the top 5-10 results matter most
- Customer support deflection where wrong-document retrieval causes wrong answers
- Code search where syntactic similarity isn't enough: need semantic relevance
- Multi-tenant SaaS retrieval where mixed-tenant noise needs precise filtering
Examples in production
Cohere
Cohere Rerank API: most widely-used managed reranking service in production RAG systems; v3 handles 100+ languages with strong quality.
SourceBAAI
BGE-reranker (open source): strong open-source reranker, BGE-reranker-large is competitive with managed rerankers on English benchmarks.
SourceStanford CS
ColBERT (Khattab & Zaharia, 2020): late-interaction architecture that's become the foundation for several production reranking systems.
SourceReranking compared to alternatives
| Alternative | Choose Reranking when | Choose alternative when |
|---|---|---|
Larger initial retrieval Just retrieve more candidates and skip reranking | Reranking is much more accurate per dollar than just retrieving more candidates | Larger initial retrieval only when reranking infrastructure isn't feasible (latency, cost) |
Better embeddings Use a higher-quality embedding model instead of reranking | Reranking + good embeddings beats either alone | Better embeddings reduce candidate count needed but rarely replace reranking entirely |
Common pitfalls
- Skipping reranking entirely: pure ANN results are often only 60-70% precision in the top results
- Reranking too few candidates (top 10): defeats the purpose; rerank top 50-200 to give the reranker meaningful headroom
- Reranking too many candidates (top 1000+): cost and latency get out of hand without proportional quality improvement
- Using LLM-as-reranker for every query: way too expensive; reserve for high-stakes use cases
- Not measuring reranker quality on your domain: off-the-shelf rerankers vary significantly by domain
Questions about Reranking.
Cohere Rerank: ~$0.001-0.002 per query at typical retrieved-document counts. Self-hosted BGE-reranker: server cost only (one A10/A100 GPU handles thousands of queries/sec). LLM-as-reranker: 10-100× more expensive per query depending on how many candidates are scored. For most production use cases, Cohere Rerank or self-hosted BGE provides the right cost-quality balance.
Sometimes. If off-the-shelf rerankers measurably underperform on your domain (you can demonstrate this with eval data), fine-tuning a cross-encoder on domain query-document relevance pairs typically lifts quality 5-15%. This is a real engineering investment (collecting labeled relevance data, training, evaluation) that pays off for high-volume specialized applications but rarely makes sense for general use cases.
Yes: reranking is independent of the embedding model used for initial retrieval. The reranker takes raw query and document text, not embeddings. This means you can switch embedding models without re-evaluating reranker quality, and you can rerank the output of hybrid retrieval (semantic + keyword) directly.
Need help implementing Reranking?
BearPlex builds production AI systems that use Reranking for Fortune 500s and high-growth scale-ups. Outcome-based pricing. 90-day embedded sprints.