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 graph implementation, use the knowledge-graph-builder skill.

Overview

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 overwhelm the model (lost in the middle), and generation can hallucinate despite good context (low faithfulness).

Modern RAG extends beyond basic vector similarity. Hybrid search combining dense embeddings with sparse BM25 is now the baseline. Re-ranking with cross-encoders improves precision after initial retrieval. Contextual chunking and late chunking preserve document-level semantics that fixed-size chunking loses. GraphRAG enables multi-hop reasoning over entity relationships by building knowledge graphs from documents. Proposition chunking breaks documents into atomic facts for precise retrieval of individual claims.

Choose techniques based on your query complexity and document structure. Start with hybrid search and re-ranking as the foundation, then layer contextual chunking, GraphRAG, or query expansion as needed. Measure everything: Precision@K, Recall@K, faithfulness, and end-to-end latency. The difference between a good and bad chunking strategy alone can create a 9% gap in recall performance.

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