The Platform Engineering Playbook for Production LLMs
InfoQ, Monday, October 5th, 2026
A team cut production LLM hallucinations from 15% to 1.5% with platform controls like retry loops and intent gates, not a new model.
This InfoQ article describes an LLM-driven inventory recommendation system whose hallucinations fell from about 15% of responses in month one to 1.5% six months later without changing the foundation model.
Key platform levers included an automated retry loop catching formatting, grounding and infrastructure errors, and an intent-validation gate that returns 'unclassified' rather than defaulting to the highest-scoring agent.
Prompts live in a history-preserving registry for instant updates and rollback, tool authorization is enforced default-deny at the resource server, and hallucination rates and per-team token costs are instrumented at ingress.