Shipping LLM features that survive production

A demo takes a weekend. A feature customers rely on needs evaluation, guardrails and cost controls.

Most teams can get an impressive language-model prototype working in a few days. The hard part comes after: the feature has to give acceptable answers for thousands of real inputs, at a predictable cost, without leaking data or embarrassing the brand.

Start with an evaluation set before you write much code. Collect a few hundred real examples with the answers you expect, and score every change against them. Without this, prompt tweaks feel like progress while quietly breaking cases you fixed last week.

Next, decide what the model is not allowed to do and enforce it outside the prompt. Input filtering, output validation and clear fallbacks to a human or a simpler rule-based path protect users when the model is unsure.

Finally, track cost and latency per request from day one. Caching, smaller models for simple cases and retrieval that sends only the context that matters can cut costs dramatically. Treat these numbers like any other production metric, with alerts when they drift.