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Level 3 · Advanced Scale & Quality

Goal: go from "it returns results" to "it returns the right results, fast, under real load" — the retrieval-quality and performance engineering that separates a demo from a production RAG system.

Modules

  1. Hybrid Search — Dense + Sparse (BM25)
  2. Re-ranking with Cross-Encoders
  3. Chunking Strategies for Retrieval Quality
  4. Embedding Model Selection & Fine-Tuning Basics
  5. Benchmarking & Performance Tuning
  6. Caching & Latency Optimization
  7. Monitoring & Observability for Vector Search
  8. Updates, Deletes & Consistency
  9. Cost Optimization at Scale
  10. Project — Benchmark & Optimize a RAG Pipeline

By the end of this level you'll know how to measure and improve retrieval quality, tune a vector database for latency and cost, and keep it healthy under continuous writes.