Guide Tackles RAG Production Challenges: Evals, Security, and Governance

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A new technical guide on DEV Community serves as a sequel to an earlier tutorial on building a RAG system using pgvector and Gemini from scratch. The guide focuses on transitioning a working RAG or AI agent system into a production-ready deployment by addressing quality, visibility, security, and continuous improvement challenges. It spans eight chapters covering automated evaluations, observability and cost tracking, security guardrails, MLOps pipelines, fine-tuning, multi-agent architecture, and EU AI Act governance. The guide uses tools including Google Gemini API, Langfuse, GitHub Actions, and Hugging Face, most of which offer free tiers. Each chapter is designed to be read independently, though familiarity with the previous pgvector-based tutorial is recommended.

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