Rafi Atha - AI EngineerUpdated
A prototype only has to work once. A production system has to work every day, for every user, within budget. Here is what changes between the two, and what to plan for before launch.
A working AI prototype now takes days, not months. Connect a model through an API, write a prompt, and you have an assistant that answers questions, reads contracts or summarizes a meeting. The demo goes well, and someone asks when it can go live.
That is where most AI projects slow down. A demo only has to work once, on questions its builders chose. A production system has to handle whatever real users bring, follow rules the business cannot bend, and stay within budget as usage grows.
| Aspect | In the demo | In daily operations |
|---|---|---|
| Questions | Picked by the team that built it | Whatever customers and staff actually type |
| Rules | Implied in the prompt | Written policies the system must never break |
| Cost | A few test calls | Every request, every day, from every user |
| A wrong answer | Someone reruns it | A customer acts on it |
| Improvement | The builder edits the prompt | Needs feedback from the people using it |
An assistant should answer from sources you approve, such as policy documents, product data or past tickets, and say so when those sources don't cover a question. Test it with real questions, including the awkward ones, before anyone outside the project sees it.
Measure the cost of one request during the pilot and multiply it by the volume you expect. Simple tasks can go to smaller models, and repeated questions can be answered from a cache. We cover this in Token Economics.
Decide in advance when the AI stops: a question outside its sources, a customer asking for a person, or a decision the business wants a human to own. When it hands over, the conversation and everything already collected should go with it, so the customer never has to repeat themselves.
Log the questions people ask, let staff flag wrong answers, and review them on a fixed schedule. The most useful improvements come from what users actually do, not from what the builders expected.
We judge an AI system by how it handles an ordinary working day, not by how it performs in a demo.
We don't ship AI as a separate tool people have to remember to open. It goes inside the workflows teams already run: customer service queues, document processing and reporting. Success is then measured in numbers the business already tracks, such as time to resolve a request or documents processed per day.
The demo runs on chosen questions with no cost pressure. Real users bring unexpected questions, the rules have to hold every time, and running costs grow with usage. Planning for those before launch is what moves a pilot into daily use.
Limit it to approved sources, test it against real questions, and give it a clear way to say it doesn't know and hand the conversation to a person.
When a question falls outside its approved sources, when a customer asks for a person, or when a decision carries risk the business wants a human to own. The handoff should carry the whole conversation with it.
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