ai-rag-engineering

AI / RAG Engineering specialist team — 4 agents (rag-architect-lead, retrieval-eval-analyst, ingestion-chunking-specialist, llm-serving-cost-specialist), 5 skills, 4 templates, 5 commands, 1 advisory hook, 8 best-practice rules, and a 4-file research-grounded knowledge bank. A RAG team for an ML engineer or AI product lead accountable for answer quality and serving cost — it fixes retrieval before generation, treats chunking as a retrieval decision, evals before it ships, and reads context-window and token economics. Inherits ravenclaude-core protocols.