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Module 7: Advanced Retrieval Engineering Slides

Slide walkthrough for Module 7 of Production-Grade RAG Systems Engineering: Hybrid search, reranking, query expansion, and Graph RAG for...

This slide page is the visual review companion for the full course module. Use it to recap the architecture, examples, exercises, production warnings, and takeaways after reading the lesson.

Slide Outline

  1. Advanced Retrieval Engineering - Hybrid search, reranking, query expansion, and Graph RAG for production-quality retrieval
  2. Learning Objectives - 4 outcomes for this module
  3. Why This Module Matters - This is where your RAG system goes from "works in demos" to "works in production." The difference between 70% and 90% re
  4. Hybrid Search - Lesson section from the full module
  5. Reranking - Lesson section from the full module
  6. Query Expansion - Lesson section from the full module
  7. Graph RAG - Lesson section from the full module
  8. Common Mistakes to Avoid - 4 mistakes covered
  9. Hands-On Labs - 2 hands-on labs
  10. Key Takeaways - 5 points to remember

Learning Objectives

  • Implement hybrid search (BM25 + vector)
  • Add reranking with cross-encoder models
  • Design query expansion and transformation strategies
  • Understand Graph RAG for relationship-aware retrieval

Why This Module Matters

This is where your RAG system goes from "works in demos" to "works in production." The difference between 70% and 90% retrieval accuracy is the difference between a useful tool and an unreliable one. Hybrid search + reranking is the production standard.

Common Mistakes

  • Reranking all results (too slow) — rerank top-20 only
  • Not tuning the BM25/vector weight ratio for hybrid search
  • Using query expansion without controlling result diversity
  • Implementing Graph RAG before basic RAG is working well

Key Takeaways

  • Hybrid search (BM25 + vectors + RRF) improves recall by 20-40%
  • Cross-encoder reranking improves precision at the cost of latency
  • Query expansion handles ambiguous or short queries
  • Graph RAG enables multi-hop reasoning across entity relationships
  • Advanced retrieval is the highest-ROI investment in RAG quality

Hands-On Labs

  1. Implement Hybrid Retrieval

    Combine BM25 and vector search with RRF.

    35 min - Intermediate

    • Add BM25 index alongside vector index
    • Implement Reciprocal Rank Fusion
    • Compare hybrid vs single-mode on test queries
    • Measure precision/recall improvement

    View lab files on GitHub

  2. Add Cross-Encoder Reranking

    Rerank retrieval results for better relevance.

    30 min - Intermediate

    • Load a cross-encoder reranking model
    • Rerank top-20 hybrid results to top-5
    • Compare answer quality with and without reranking
    • Measure latency impact

    View lab files on GitHub

Read the full module | Back to course curriculum