Rag Architect

This is a comprehensive RAG implementation guide that goes well beyond basic vector search setup. It walks you through the full production stack: chunking strategies with overlap and semantic boundaries, embedding selection and indexing with deduplication, hybrid search combining dense retrieval with BM25, reranking with Cohere, and proper evaluation metrics like precision@k and recall@k. The checkpoints after each step are genuinely useful, and the "must not" constraints call out common mistakes like blindly using 512 token chunks. If you're building a RAG system that needs to actually work at scale rather than just demo well, this covers the architectural decisions and implementation patterns that matter.