Anyone can build a demo with a language model in an afternoon. The challenge is shipping something that holds up under real users, real data, and real edge cases.
Evaluations Beat Vibes
If you cannot measure quality, you cannot improve it. Build an evaluation set that reflects your real users and run it on every change.
Cache What You Can
Prompt caching and structured output caches turn expensive token bills into pennies. They also make your product feel instant.
Plan for Failure
Models hallucinate, providers go down, and tokens get truncated. Build graceful fallbacks and design the UI so the user is never left guessing.
Conclusion
Treat your LLM feature like any other system: measurable, observable, and defensible. The teams that do are the ones whose AI features survive their second month in production.