4 min readAgentic AI in Practice
Shipping Agentic RAG to Production
What changes when a RAG system stops being a demo and has to serve real users: retrieval quality, tool use, evaluation, and guardrails.
#rag#agentic-ai#llm#mlops
From demo to production
Retrieval-augmented generation looks easy in a notebook and hard in production. The gap is mostly engineering: retrieval quality, evaluation, latency, and guardrails.
Retrieval is the product
Most RAG failures are retrieval failures, not model failures. Invest in chunking, hybrid search, and re-ranking before reaching for a bigger model.
Evaluate continuously
Build an offline eval set early. Track answer faithfulness and retrieval hit-rate on every change, the same way you would track test coverage.
Guardrails by construction
Prefer read-only data flows and constrained tool use. The smaller the write surface, the smaller the attack surface.