Developer Shares 27-Point Checklist for Building Production-Ready RAG Systems

A developer who built a Retrieval-Augmented Generation (RAG) system from scratch has published a practical checklist covering 10 key lifecycle stages that go far beyond the basic chunk-embed-search pipeline. The checklist addresses critical areas including document indexing, embedding model management, hybrid search strategies, inference performance, and context compression. It also highlights operational concerns such as cost-per-query tracking, CI/CD pipelines, secrets management, and stage-level latency monitoring. The author emphasizes that evaluation metrics like retrieval precision, faithfulness checks, and caching strategies are essential for a system to move from prototype to production. The post concludes that developers need not implement everything on day one, but awareness of all lifecycle components is what separates a working demo from a reliable system.