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Session 3 - Module A: Index Algorithms

⚠️ ADVANCED OPTIONAL MODULE Prerequisites: Complete Session 3 core content first.

You've implemented production-grade vector search with HNSW indexing and hybrid search in Session 3. But when your RAG system scales to millions of documents with specialized domain requirements, you hit the limits of generic vector databases. Off-the-shelf solutions optimize for general similarity search, not the unique patterns of question-answering workloads.

This module teaches you to implement custom indexing algorithms that understand RAG-specific needs: semantic clustering for topic-focused retrieval, dynamic parameter optimization that adapts to query patterns, and multi-signal ranking that combines semantic similarity with keyword matching and recency weighting. These aren't just performance optimizations – they're intelligence upgrades that make your system fundamentally better at finding the right information for generation.

📝 Multiple Choice Test - Session 3

Test your understanding of advanced index algorithms:

Question 1: What is the key advantage of custom HNSW implementation for RAG?
A) Reduced memory usage
B) RAG-specific optimizations like semantic clustering and keyword integration
C) Faster build times
D) Simpler configuration

Question 2: Why is dynamic index optimization important?
A) It reduces storage costs
B) It adapts index parameters based on actual query patterns for better performance
C) It simplifies maintenance
D) It reduces memory usage

Question 3: How does semantic clustering improve RAG performance?
A) It reduces index size
B) It groups similar content for more efficient search within relevant topics
C) It speeds up indexing
D) It reduces computational requirements

Question 4: What is the benefit of hybrid indexing (vector + keyword + temporal)?
A) Reduces complexity
B) Enables multi-dimensional optimization for semantic, exact match, and recency needs
C) Reduces memory usage
D) Simplifies implementation

Question 5: Why is RAG-optimized search different from general vector search?
A) It's always faster
B) It combines semantic similarity with domain-specific factors like keywords and recency
C) It uses less memory
D) It's simpler to implement

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