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Practice 20AdvancedImplementationAbout 18 minutes

Corresponding knowledge: Complementary retrieval and rank fusion in hybrid search

Vector search always misses product numbers. How to design BM25, vector recall and reranking?

Examine hybrid retrieval, rank fusion, deduplication and multi-stage evaluation.

Learn corresponding knowledgeInterview questions · Answer independently →
BM25Hybrid SearchRRF

Knowledge content check2026-10-03 · Check the source of the original question2026-10-02

The knowledge corresponding to this exercise:

From document ingestion to cited RAG answers · Hierarchical deep learning →RAG evidence flow and failure diagnosis · Hierarchical deep learning →
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Knowledge exercises·Independent answers

My notes and review ↗

Principles and Solutions have been collapsed. Explain the core mechanism, boundaries and verification methods in your own words, and then compare them.

Answers and personal notes

Each modified commit will be kept as an independent history. Your level of mastery is up to you to evaluate yourself against the standards.

Explain in your own words first

The core principles, analysis, Q&A and migration cases have been closed. When you are ready, unfold it and compare it with the content to find any omissions.

Hands-on verificationComplete on demand · Suggestions15 minutes

Hand-calculated RRF for two-way sorting A,B,C and B,D,A, illustrating how duplicate documents are merged.

Expand acceptance requirements and checkpoints
  • Merge scores for the same document
  • Documents that are not candidates cannot be rearranged and filled in.
  • Rankings are counted from a unified starting point

Key inspections

  • Know that lexical and vector signals are complementary
  • Can explain fusion and reranking of boundaries
  • Evaluate candidate coverage individually