Getting Started with Vector Search and Embeddings

Why it matters

Retrieval quality depends on the complete path from representation to indexed candidates, not on a vector database label alone.

What to practise

Generate embeddings, create and deploy an index, issue queries, and inspect which configuration choices affect candidate recall and operational cost.

How it fits

Use the lab after subsection 3.2’s retrieval lifecycle, chunking, precision, recall, and reranking Bites. Keep candidate generation separate from downstream ranking and grounding.