Using AI at Scale Is Not the Same as Using ChatGPT

There is a difference between using AI casually and building AI into a product, and most of the confusion around AI features comes from missing it.

When you open ChatGPT or Claude and ask a question, you are solving one particular problem. You want this email rewritten, this document summarised, this idea explored. You look at the answer, and if it is not quite right, you ask again.

Building AI into a product is different. You are trying to solve one problem many times. You write one set of instructions, and that same set of instructions has to summarise many meeting transcripts, or categorise many emails, for many users. You are solving a category of problems, not a single one.

The instructions stay the same, the inputs do not

When AI is integrated into a product, you define one set of instructions, and that set has to handle every input the users might send. These inputs are the part you cannot control. A meeting transcript might be five hours long, or it might be one minute long with nobody having said anything. The input can be empty, really long, really short, something you expect, or something you do not expect.

So the instructions have to cover as many of those cases as possible, because you do not get to choose what comes in.

What the chat interface hides from you

The chat interfaces we use every day are themselves applications. They have a place for you to type your problem, and behind the scenes they have a set of rules that help them return the best answer. We do not know exactly what those rules are, but they include things like not generating inappropriate content, not giving instructions for destructive behaviour, and sometimes asking you a question back if they do not understand.

When you integrate a large language model directly into your own application, you talk to the model through its API, without those guardrails in place. Everything you want the model to know about the problem you are solving, you have to specify explicitly.

That is what building an AI product actually means: designing these behind-the-scenes instructions so you get the best results at the end.

System prompt and user input

The instructions you write are called the system prompt. This is the part you do prompt engineering on. It holds the general instructions for solving the problem, the context that matters for your business, and your notes to the model on how to handle edge cases or things it does not understand.

The user inputs are the concrete examples you run those instructions on, the emails or the transcripts, and there are infinite possibilities for what they can be.

Why this is hard

AI is non-deterministic. If you got the same answer 100 times, the 101st answer might be different, because it is based on probabilities. The answers seem smart, but the model is putting together a probable sequence of words from its training data.

That is why a feature that looks fine when you try it once can behave differently the next time, and why building with AI at scale takes a different approach than typing a question into a chat box.