Key Topics:

• Overview and Rationale: Why vector databases exist, how they differ from traditional search, and the role they play in grounding LLM applications.
• Search Types: Vector search, text search, and hybrid search — when to use each and what you lose by picking the wrong one.
• Indexing Techniques: Product Quantization (PQ), Locality Sensitive Hashing (LSH), and Hierarchical Navigable Small World (HNSW) — the tradeoffs between speed, memory, and recall.
• Retrieval Techniques: Cosine similarity, nearest neighbor search, and how retrieval quality degrades at scale without the right index design.
• Advanced RAG Techniques: Chunking and filtering strategies, query rewriting, hybrid search, auto-cut, and re-ranking for precision at the top of the result set.
• Embedding and Model Selection: Domain-specific embeddings, fine-tuned retrieval models, and compression approaches including scalar quantization, product quantization, and binary and matryoshka methods.
• Adaptive Retrieval and Multi-Tenancy: Multi-phase rescoring, preserving recall under compression, tenant isolation, and resource allocation patterns for shared environments.
• Production Challenges: Scaling, reliability, and cost optimization — the problems that don’t surface until you’re running real query volumes.
• Hands-On Exercises: Vector search, similarity search, hybrid search, generative search, Weaviate Query Agent, multi-tenancy, vector compression, and semantic caching.