Reranking in RAG pipelines

Why reranking matters, how to design it, and how to ship it safely in production RAG systems.

TL;DR

Retrieval gets you recall. Reranking gets you precision. Without it, your LLM wastes context budget and answers degrade under load.

What reranking is

A reranker reorders the top-K retrieved chunks with a stronger relevance model (typically a cross-encoder or a re-ranker LLM), producing a smaller, higher‑quality set for the generation step.

When you need it

You see irrelevant snippets in the final context.
The vector index is large and recall-heavy.
You’re operating with strict context limits.

Typical pipeline

Retrieve top-K using embeddings (fast, high recall).
Rerank K→N using a cross-encoder (slower, higher precision).
Select top-N for prompt assembly.

Practical defaults

Stage	Default
K (retrieve)	50–200
N (final)	6–12
Reranker	Cross-encoder (BGE/ColBERT class)

Metrics to watch

Recall@K (retriever)
MRR / nDCG (reranker)
Answer quality on a fixed golden set
Latency budget (P95)

Shipping safely

Example prompt layout

System: You answer using only the provided context.
 
Context:
[1] ...
[2] ...
[3] ...
 
Question: ...

Final note

Reranking is the most cost‑effective upgrade in RAG. It improves quality faster than increasing model size or adding more context.