RAG & Retrieval Engineering

Build retrieval systems that are grounded, observable, and explicit about tradeoffs.

This skill covers:

• Retrieval architecture choice: long-context vs hosted file search vs tool-first/MCP vs SQL/graph vs classic vector RAG
• Corpus preparation: parsing, metadata, chunking, ACLs, freshness, invalidation
• Retrieval quality: sparse, dense, hybrid, late interaction, reranking, multimodal retrieval
• Answer quality: grounding, citation coverage, refusal on missing evidence, regression testing

July 2026 posture