How to Improve RAG Answers With Reranking

Vector search is fast but imprecise. It finds documents that are semantically similar to the query, but 'similar' and 'relevant' are not the same thing. Reranking adds a second, more precise step that reorders the results by actual relevance.

This guide shows how to add reranking to an existing RAG pipeline and explains when it helps most.

Quick answer

After vector search retrieves candidate chunks (e.g., top 20), pass them through a cross-encoder reranker that scores each chunk against the query. Take the top 3-5 reranked results for generation. This typically improves answer quality by 10-30%.

• Your RAG system retrieves plausible but wrong chunks.
• Vector search returns results that are similar in topic but not relevant to the specific question.
• You have tried improving chunking and embeddings but still get mediocre answers.

Why vector search is not enough

Vector search using embeddings compresses entire passages into fixed-length vectors. This compression loses nuance — two passages about the same topic get similar vectors even if one answers the question and the other does not.

Reranking uses a cross-encoder that reads the query and each candidate passage together, producing a much more accurate relevance score. It is slower but more precise.

How reranking works

The pipeline becomes: embed query → vector search (retrieve top 20-50) → rerank (score each against query) → take top 3-5 → generate answer.

The reranker is a cross-encoder model that takes (query, passage) pairs and outputs a relevance score. Unlike bi-encoders used in vector search, cross-encoders see both texts together and can capture fine-grained relevance.

• Retrieve a larger initial set from vector search (20-50 candidates)
• Score each candidate with the cross-encoder reranker
• Sort by reranker score and take the top 3-5
• Pass the reranked results to the generation model

Choosing a reranker

For local use, cross-encoder models from sentence-transformers work well (e.g., `cross-encoder/ms-marco-MiniLM-L-6-v2`). For cloud use, Cohere Rerank and Jina Reranker are popular hosted options.

The reranker does not need to be large. A small cross-encoder model (50-100MB) can dramatically improve results because it is doing a different, more focused task than the embedding model.

from sentence_transformers import CrossEncoder

reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")

# After vector search returns candidates
candidates = vector_search(query, top_k=20)

# Rerank
pairs = [(query, chunk["text"]) for chunk in candidates]
scores = reranker.predict(pairs)

# Sort by score and take top 5
ranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
top_chunks = [chunk for chunk, score in ranked[:5]]

When reranking helps most

Reranking helps most when your document collection is large, when queries are specific, and when the embedding model produces many 'near-miss' results that are topically related but not specifically relevant.

If your RAG system already retrieves the right chunks most of the time, reranking will not help much. Test retrieval quality first before adding reranking.

Performance considerations

Reranking adds latency — typically 50-200ms for 20 candidates with a small cross-encoder. This is acceptable for most applications but matters for real-time chat.

If latency is critical, reduce the number of candidates passed to the reranker (10 instead of 50) or use a faster reranker model.

Worked example: improving a knowledge base RAG system

A company knowledge base RAG system retrieves the right document only 60% of the time. After adding a cross-encoder reranker that rescores the top 20 vector search results, the correct document appears in the top 3 results 85% of the time. Answer quality improves noticeably because the generation model now has better context.

Common mistakes

• Adding reranking before testing whether retrieval is the actual bottleneck.
• Using too few initial candidates (reranking 5 results adds little value — you need 20+).
• Not considering the latency cost of reranking in real-time applications.

When to use something else

If your problem is chunking quality rather than retrieval precision, see chunking documents for RAG. For building the RAG pipeline from scratch, see building a RAG app.

How to apply this in a real AI project

How to Improve RAG Answers With Reranking becomes much more useful once it is tied to the rest of the workflow around it. In real work, the result depends on model selection, prompt design, tool integration, evaluation, and the operational reality of shipping AI features, not only on following one local tip correctly.

That is why the biggest win rarely comes from one clever move in isolation. It comes from making the surrounding process easier to review, easier to repeat, and easier to hand over when another person inherits the workbook or codebase later.

• Test with realistic inputs before shipping, not just the examples that inspired the idea.
• Keep the human review step visible so the workflow stays trustworthy as it scales.
• Measure what matters for your use case instead of relying on general benchmarks.

How to extend the workflow after this guide

Once the core technique works, the next leverage usually comes from standardising it. That might mean naming inputs more clearly, keeping one review checklist, or pairing this page with neighbouring guides so the process becomes repeatable rather than person-dependent.

The follow-on guides below are the most natural next steps from How to Improve RAG Answers With Reranking. They help move the reader from one useful page into a stronger connected system.

• Go next to How to Chunk Documents for Better RAG Results if you want to deepen the surrounding workflow instead of treating How to Improve RAG Answers With Reranking as an isolated trick.
• Go next to How to Build a RAG App With Your Own Documents if you want to deepen the surrounding workflow instead of treating How to Improve RAG Answers With Reranking as an isolated trick.
• Go next to How to Evaluate AI Outputs in Real Apps if you want to deepen the surrounding workflow instead of treating How to Improve RAG Answers With Reranking as an isolated trick.

Related guides on this site

These guides cover RAG pipeline building, chunking strategies, and output evaluation.

• How to Chunk Documents for Better RAG Results
• How to Build a RAG App With Your Own Documents
• How to Evaluate AI Outputs in Real Apps