From Prompt to Production: Deploying LLMs in Enterprise

The demo always works. You show the stakeholders, they're impressed, someone says 'can we have this in production by next month'. And then the real work begins.

The Gap Between Demo and Production

Demos are optimized for the best case. Production has to handle every case. The inputs are messier, the edge cases are weirder, the latency requirements are tighter, and the cost at scale is real money. None of that shows up in a Jupyter notebook.

Evaluation Before Everything

The single biggest mistake I see teams make: they deploy before they can measure. If you don't have an eval set — a collection of inputs with known-good outputs — you have no idea whether a model change made things better or worse. Build the eval harness first, even if it's just 50 examples. Especially if it's just 50 examples.

You cannot improve what you cannot measure. This is not new wisdom. It just gets forgotten every time a new model comes out.

Prompt Engineering Is Engineering

Treat your system prompts like code. Version control them. Review changes. Test them against your eval set before deploying. The prompt is the logic layer of your application — it deserves the same rigor as any other component.

Latency and Cost

At prototype scale, latency is invisible and cost is irrelevant. At production scale, both will surprise you. Cache aggressively. Use smaller models where the task allows it. Batch where real-time isn't required. These aren't premature optimizations — they're the difference between a sustainable product and one that gets shut down after the first billing cycle.