LLMOpsss patterns that survive production

Shipping an LLM feature is the easy part. Keeping it reliable in production — with cost controls, evaluation loops, and clear ownership — is where most teams struggle.

Start with a thin evaluation harness: golden prompts, regression suites, and human review queues for high-risk outputs. Instrument latency, token cost, refusal rates, and user escalation so you can catch drift before customers do.

Treat model and prompt changes like any other release. Version configs, gate rollouts behind feature flags, and keep a rollback path that does not require a full app deploy.

When reliability and observability are built into the loop, LLM systems stop feeling like experiments and start behaving like products your business can trust.