rag-implementer

Build production-ready retrieval-augmented generation systems. RAG = Retrieval + Context Assembly + Generation. Use RAG when LLMs need access to fresh, domain-specific, or proprietary knowledge not in their training data. Do not use RAG when simpler alternatives (FAQ pages, keyword search, semantic search) suffice. For KB architecture selection and governance, use the knowledge-base-manager skill. For knowledge gr...

Before building RAG, validate the need: try FAQ pages, keyword search, concierge MVP, or simple semantic search first. Only proceed with RAG for 50k+ documents with validated user demand and $200-500/month budget. RAG systems range from Naive (prototype) through Advanced (production) to Modular (enterprise), each tier adding complexity and cost.

The RAG pipeline has three core stages. First, retrieval finds relevant documents using hybrid search (semantic + keyword). Second, context assembly ranks, deduplicates, and compresses retrieved chunks into an optimal prompt. Third, generation produces a grounded response with source attribution. Each stage has distinct failure modes: retrieval can miss relevant documents (low recall), context assembly can overwhe...