Skip to main content

​
What is Reranking?

Two-stage retrieval for better accuracy:
  1. Stage 1 (Fast Retrieval) - Maximize Recall:
    • Goal: Don’t miss relevant documents - cast a wide net
    • Method: Retrieve many candidates (e.g., top-50) using fast methods like lexical or vector search
    • Trade-off: Fast but includes some noise/irrelevant results
  2. Stage 2 (Reranking) - Maximize Precision:
    • Goal: Keep only the truly relevant documents - filter out the noise
    • Method: Use a more accurate cross-encoder model to deeply analyze each candidate and rerank them
    • Trade-off: Slower but much more accurate at identifying relevance
Why it works:
  • Fast retrievers are good at finding candidates but not great at ranking them
  • Cross-encoders are excellent at ranking but too slow to run on thousands of documents
  • Combining both gives you speed + accuracy
Result: Better quality documents sent to your LLM → better answers

​
Minimal rerank pipeline

​
Integrating with your retriever

The complete pipeline shows:
  1. First-pass retrieval (lexical + semantic) - Cast a wide net (top-50)
  2. Reciprocal Rank Fusion - Merge the results
  3. Cross-encoder reranking - Keep only the best (top-5)
Latency/cost tips:
  • Keep candidate pool small (e.g., 20-100) and final top_k small (3-5).
  • Run first-pass lexical + semantic in parallel; batch rerank scoring for throughput.
  • Log latency and token/call costs during the lab.

​
Practical Exercise (15 min)

# Your task:
# 0) Corpus: use chunks from 'Assets/paul_graham_essay.txt' (treat each chunk as a doc)
# 1) Use HybridRetriever to get top-50 for 5 queries.
# 2) Rerank with CrossEncoderReranker to top-5.
# 3) Compute NDCG@10, MRR before vs. after reranking.
# 4) Record end-to-end latency; note candidate pool size impact.
#
# Expected insights:
# - Reranking lifts precision-focused metrics.
# - Most gains come from better ordering of already-relevant docs.
# - Larger candidate pools help recall but increase latency.
#
# References:
# - Pinecone Learn (2025): https://www.pinecone.io/learn/retrieval-augmented-generation/
# - Practitioner write-up (2025): https://dj3dw.com/blog/the-power-of-reranking-in-retrieval-augmented-generation-rag-systems/

​
Production checklist callout

  • Retrieve top-50 in parallel (lexical + semantic) → cross-encoder rerank top-20 → keep top-5 for generation.
  • Log per-stage latency and cost; adjust candidate pool and rerank depth to stay within budget.