Every AI project starts the same way: someone sees a demo that works beautifully and says "just add AI" to their product. I have been on both sides of that conversation. Here is what actually happens between the demo and production.
1. Latency is a feature, not a bug
A demo that takes 10 seconds to respond feels magical. A production system that takes 10 seconds loses users. I built a real-time translation booth that needed to go from speech input to translated speech output in under two seconds. That meant streaming speech recognition, LLM translation, and speech synthesis all had to overlap, not run sequentially.
The lesson: budget your latency before you write code. If you do not know your latency target, you do not know your architecture.
2. Evaluation is not optional
In a demo, you test on the cases you know work. In production, users find every edge case within the first week. I now write evaluation scripts before writing the feature. A simple test set of 50 examples, run automatically on every change, catches more problems than any amount of manual testing.
3. Fallbacks are the product
The primary path is the demo. The fallback paths are the product. When the model does not understand an accent, when the API is slow, when the network drops — that is when your system proves itself. I build fallback chains: try the fast model first, fall back to the slow one, fall back to a template, fall back to a human.
4. Token costs are real
A demo that calls GPT-4 costs nothing. A production system that calls GPT-4 ten thousand times a day costs real money. I track token costs per feature and set hard budgets. If a feature cannot survive on a cheaper model, it does not ship until it can.
5. The gap is where value lives
The space between demo and production is where most AI companies fail. It is also where the real value is. Anyone can build a demo. Building a system that works reliably, at scale, within budget — that is the hard part, and it is the part clients actually pay for.