Model calls fail, drift, and cost money. The engineering that turns a demo into a dependable production feature.
A demo that works once is easy. A feature that works on every request, for every user, when a provider is having a bad day, is engineering. The gap between the two is where most AI features quietly break, and closing it is what LLM reliability and cost engineering means in practice.
Failover
Any single LLM provider will have outages, rate limits, and slow days. Routing across more than one provider — with sensible fallbacks — means one provider's bad hour is not your product's bad hour.
Structured outputs
If your code needs a decision, an amount, or a category back from the model, parsing free text and hoping is a bug waiting to happen. Enforcing a structured, machine-usable output turns "usually works" into "works."
Evals
You cannot improve what you do not measure. A small evaluation harness — real inputs, expected behavior — catches quality regressions before your users do, and lets you cut latency and cost with confidence instead of guesswork.
None of this is glamorous, and all of it is the difference between an AI feature you can depend on and one you cross your fingers over.