LLMs in production: reliability patterns beyond the demo

A demo has one job: work, once, on a case someone chose. Production has a much harder job: keep working, on cases nobody chose, indefinitely.

The gap between the two is rarely the model's raw capability — it's the handling around it. What happens when the model's response doesn't parse as expected? When it's confidently wrong instead of visibly uncertain? When the underlying provider has an outage or changes behavior without warning?

The patterns that hold up in production are unglamorous: strict validation of model output before it touches anything downstream, a fallback path for when validation fails, and logging detailed enough to reconstruct exactly what the model saw and said, not just whether the request succeeded.

Cost and latency also behave differently at scale than in a pilot. A prompt that's fine for ten requests a day can become a real budget line and a real user-facing delay at ten thousand — which means production LLM systems need the same kind of load and cost modeling as any other backend service, not an exemption because the technology is new.

None of this makes the technology less useful. It makes it a system that needs engineering discipline, the same as any other piece of production infrastructure — and teams that treat it that way ship things that keep working after launch day.