Guide Outlines 22-File Framework for Taking AI RAG Systems to Production

A technical guide published on DEV Community details a structured approach for AI architects to move retrieval-augmented generation (RAG) systems from prototype to production. The framework spans seven phases — evaluations, observability, security, MLOps, fine-tuning, multi-agent coordination, and governance — implemented across 22 files. Security is addressed through four layered defenses, while automated quality gates on GitHub require an overall score of at least 75% before any deployment proceeds. Fine-tuning via LoRA trains only 0.09% of model parameters and completes in under two minutes on a CPU. The guide also covers EU AI Act compliance, classifying the RAG system as limited risk with a score of 0.18, and implementing required audit logging and AI disclosure measures.