RAG Optimization 2 · Re-ranking
Find the most relevant docs before the LLM.
Re-ranking finds the most relevant documents before sending them to the LLM. After hybrid search returns many results, a cross-encoder re-ranks to pick the best few — cutting cost and boosting accuracy.
In simple terms
From ~200 retrieved results, re-rank down to the ~10 most relevant.
How it works
- 1Hybrid search returns high-recall results (e.g. top 200).
- 2A re-ranker (cross-encoder) understands the full query and each document.
- 3It scores relevance more accurately and filters out noise.
- 4Keep the top 10 high-precision results.
- 5Send only those to the LLM.
Key points
- Removes less relevant / noisy documents.
- Higher-accuracy answers with fewer tokens sent to the LLM.
- Typical impact: 40–60% lower latency, 60–80% lower LLM cost, 20–40% higher accuracy.
- Cross-encoders read query + document together for better scoring.
Why it matters
Re-ranking is one of the highest-ROI RAG steps — dramatically cutting cost and improving accuracy by sending the model only what truly matters.
Frequently asked questions
- How much does re-ranking help?
- Often big drops in latency and cost with higher accuracy, because far fewer, better chunks reach the LLM.
- Bi-encoder vs cross-encoder?
- Bi-encoders (retrieval) are fast; cross-encoders (re-ranking) are more accurate because they read query and doc together.
More in RAG & Retrieval (Enterprise)
Where Hallucinations Come From
Failures across the query, retrieval and model layers.
RAG Retrieval Optimization Layer
Retrieve the right context with high recall and relevance.
RAG: Retrieval Re-ranking
Re-rank retrieved docs to keep only the most relevant.
RAG Optimization 1 · Hybrid Search
Keyword + semantic search for higher recall.
RAG Optimization 3 · Context Pruning
Remove noise; keep only what matters.
RAG Optimization 4 · Semantic Cache
Stop asking the same question twice.