Building an LLM-powered demo has gotten remarkably easy — a prompt, an API call, and a plausible-looking result in an afternoon. The gap between that and a system a business can actually depend on is where most of the real engineering work lives, and it's rarely visible in the demo.
Handling the failure cases
A demo is judged on its best outputs. A production system is judged on its worst ones — the malformed input, the edge case the model handles badly, the moment it confidently produces something wrong. Production AI systems need validation layers, confidence thresholds that route uncertain cases to a human, and monitoring that catches degradation before a customer does.
Cost and latency at scale
A single API call during development costs nothing to think about. At production volume, model choice, prompt length, and caching strategy become real line items — and the cheapest model that meets the accuracy bar is usually the right one, not the most capable one.
Data handling and governance
What data reaches the model, how it's logged, and who can access the outputs are governance questions that don't show up in a prototype but absolutely show up in a compliance review. We build these in from the start rather than retrofitting them once a system is already handling real customer data.
