A striking number of AI prototypes never make it to production, and it's rarely because the underlying capability wasn't real. It's because the jump from "this worked in a demo" to "this runs reliably, every day, on real inputs, with acceptable cost and latency" is a genuinely different engineering problem than building the prototype was.
The prototype answered a different question
A prototype's job is to answer "can this work at all." It's usually built with the happiest-path inputs, a generous budget for API calls, and no real users depending on its uptime. A production system has to answer a much harder question: does this work reliably enough, cheaply enough, and fast enough, across the full range of real inputs, to be worth depending on. Those are different bars, and clearing the first one tells you very little about the second.
Cost and latency need to be measured, not assumed
A prototype calling an LLM API a handful of times during a demo doesn't reveal what that same call pattern costs at real production volume, or what happens to user-perceived latency when that call is in the critical path of a page load. Before committing to an architecture, model production-realistic volume: how many calls per day, what's the actual latency budget for the user experience, and does the model choice and prompt design fit within both.
Evaluation needs to be systematic, not anecdotal
"I tried a few examples and it seemed good" is how most prototypes get evaluated, and it's not sufficient for a production decision. Before shipping, build a proper evaluation set — a representative sample of real or realistic inputs, including known-hard edge cases — and measure accuracy against it systematically. This also gives you a regression check: when you change a prompt or swap a model, you can verify you didn't quietly make things worse for cases the demo never covered.
Decide what "good enough" means before you ship
AI systems rarely reach 100% accuracy, and chasing it can delay a launch indefinitely. The more useful question is: what accuracy is good enough for this specific use case, given the cost of a mistake and the review process around it? A system assisting with an internal research task can tolerate more uncertainty than one that directly triggers a customer-facing action. Defining this threshold explicitly, rather than implicitly aiming for "as good as possible," is what lets a team actually ship.
Plan the human-in-the-loop path from the start
Very few production AI systems should operate with zero human oversight from day one. Design the review and escalation path as a first-class part of the system, not an afterthought bolted on when something goes wrong — and treat "how confident is the system, and what happens when it's not confident enough" as a core design question from the beginning, not a patch applied after launch.
The gap between prototype and production isn't a reason to avoid shipping AI features. It's a reason to budget real engineering time for the parts a demo never has to prove: reliability, cost, evaluation, and a defined path for the cases the model gets wrong.