Module 2: Foundations of Search & Retrieval Slides
Slide walkthrough for Module 2 of Production-Grade RAG Systems Engineering: BM25, TF-IDF, vector search fundamentals, and similarity metrics. Covers...
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
- Foundations of Search & Retrieval - BM25, TF-IDF, vector search fundamentals, and similarity metrics
- Learning Objectives - 4 outcomes for this module
- Why This Module Matters - RAG is only as good as its retrieval. If you retrieve the wrong documents, the LLM generates answers from irrelevant con
- Keyword Search: BM25 and TF-IDF - Lesson section from the full module
- Vector Search: Semantic Similarity - Lesson section from the full module
- Similarity Metrics - Lesson section from the full module
- Common Mistakes to Avoid - 4 mistakes covered
- Hands-On Labs - 2 hands-on labs
- Key Takeaways - 5 points to remember
Learning Objectives
- Understand information retrieval fundamentals
- Implement keyword search with BM25 and TF-IDF
- Understand vector search and similarity metrics
- Compare keyword vs semantic search tradeoffs
Why This Module Matters
RAG is only as good as its retrieval. If you retrieve the wrong documents, the LLM generates answers from irrelevant context. Understanding search fundamentals — keyword vs semantic, precision vs recall — is the foundation of every production RAG system.
Common Mistakes
- Using only semantic search (misses exact terms, acronyms, product codes)
- Using only keyword search (misses meaning, synonyms, paraphrases)
- Not evaluating retrieval quality separately from generation quality
- Assuming more retrieved documents = better answers (often the opposite)
Key Takeaways
- BM25 matches exact terms — fast but misses synonyms
- Vector search matches meaning — finds semantic matches but misses exact terms
- Cosine similarity is the standard metric for text embeddings
- Neither approach alone is sufficient — hybrid search combines both (Module 7)
- Understanding retrieval fundamentals is essential before building RAG
Hands-On Labs
-
Implement Keyword Search with BM25
Build a keyword search engine from scratch.
25 min - Beginner
- Load a document corpus
- Tokenize and index with BM25
- Query and rank results
- Observe limitations with synonym queries
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Implement Semantic Search
Build vector-based semantic search.
30 min - Beginner
- Generate embeddings with sentence-transformers
- Store vectors in memory
- Query with cosine similarity
- Compare results with BM25 on same queries