Guide
What Is a Reranker in RAG?
A reranker is a second-stage retrieval model that reorders candidate documents after vector search so the most useful context reaches the generation model.
Who this is for
Developers building RAG systems with Qdrant, Chroma, pgvector, BGE, E5, or similar retrieval stacks.
Recommended stack
- Embedding model for first-stage retrieval
- Vector database such as Qdrant, Chroma, or pgvector
- Reranker such as BGE Reranker v2 M3
- Evaluation set with representative questions and expected sources
Where a reranker fits
Most RAG systems first retrieve a broader set of candidate chunks with vector search. A reranker then scores those candidates against the query and returns a smaller, better ordered set.
When to add one
Add a reranker when vector search returns relevant chunks but orders them poorly, or when the answer model receives too much mediocre context.
What to measure
Measure retrieval precision, answer quality, latency, and cost. A reranker is useful only when quality gains justify the extra step.
Practical recommendations
- Start with a baseline vector search setup before adding a reranker.
- Use real queries and known-good source documents for evaluation.
- Compare BGE, E5, and other retrieval candidates on your own corpus.
Tradeoffs
Rerankers can improve retrieval quality, but they add latency and another model dependency. Keep the pipeline simple until retrieval quality requires the second stage.
Related links
FAQ
Is a reranker required for every RAG app?
No. Many simple RAG apps can start with embeddings and vector search. Add a reranker when retrieval quality needs it.
Sources
Next steps
Use the model and tool directories to choose the concrete pieces for your local AI stack. Sponsor and affiliate placements will be added later.