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This article details a production strategy for managing Large Language Models by treating the LLM stack as platform infrastructure rather than an application feature. Key metrics such as hallucination rates, token costs, and semantic degradation are addressed through automated retry loops, prompt registries, and strict authorization policies. The approach demonstrates how engineering controls can drastically reduce error rates without relying on better foundation models.
Article: The Platform Engineering Playbook for Production LLMs
In this article, author discusses his experience with AI agent hallucinations in an inventory recommendation system and how this problem was solved by treating the LLM stack as a platform infrastructure concern instead of as an application one. He makes a case for a shared LLM platform with common services like prompt registry & versioning, schema enforcement and token cost attribution by request. By Aditya Mulik
Hallucination rate is a platform-controllable metric. By wrapping the model in an automated retry loop that catches formatting, grounding, and infrastructure errors on the fly, we cut production hallucination rate from fifteen percent to 1.5 percent without touching the foundation model. An intent-validation gate that returns "unclassified" instead of defaulting to the highest-scoring agent eliminates off-intent hallucinations and improves multi-agent routing. By storing prompts in a history-preserving registry rather than hardcoding them into our application files, we can update or roll back instructions instantly at runtime. Tweaking prompts without a clear audit trail is the easiest way to silently break your AI's behavior. Enforce tool authorization directly at the resource server with a strict default-deny policy; relying solely on API gateway checks, means any single bug in your orchestrator can instantly expose every connected tool. Standard APM tools cannot detect semantic degradation. You must instrument hallucination rates and per-team token costs at request ingress. Trying to retrofit this attribution onto a live production path later will cost you weeks of engineering time. By the end of our first month running an LLM-driven inventory recommendation system in production, roughly fifteen percent of agent responses were hallucinations, confident-sounding outputs grounded in nothing…
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