Create AI Products with ChatGPT

Define a narrow product

A product definition answers four questions: who it is for, what job it does, what it takes in, and what it gives back. A GPT that drafts product descriptions for small online shops, from a list of features, is a product. A GPT that helps with everything about business is not; it is a conversation with no edge. Narrowness is what makes testing possible, because you can tell whether the job was done well.

Ask ChatGPT to challenge the definition: who else might use it, what inputs will break it, and what would a user do if the output were wrong. Those answers become your instructions and your test cases, and they save you from discovering the weaknesses only after launch.

Build a GPT for one job

Building a GPT is mostly writing instructions: the role, the task, the input it expects, the output format, and the rules for missing or unclear information. Add reference material only if it genuinely helps, such as examples of good output or a style guide. Keep the instructions short enough that you can read them in one sitting; instructions you have not read are instructions you cannot debug.

Instructions for a GPT that drafts product descriptions for a small shop:
Role: Write a product description from the features I paste in.
Rules: Use only the features given. Do not invent materials, sizes, or benefits.
Format: One headline under 10 words, then two short paragraphs, then three bullet points of key features.
Tone: plain and factual. If a feature is missing, write [MISSING] and continue.

Test the GPT on real examples, including awkward ones: incomplete features, unusual products, and inputs in the wrong format. Fix the instructions for the failures you actually see, one at a time, and keep a set of test inputs to re-run after each change.

Design behaviour, not just answers

A product's behaviour is what it does when things go wrong: when input is missing, when a request is outside its job, and when it is unsure. Write those behaviours into the instructions explicitly. A GPT that says it cannot help with a request, and suggests what it can do instead, is more useful than one that guesses its way into a wrong answer.

Also design the first interaction. A new user should understand, within seconds, what the product does and what to give it. A short opening message with an example input does more for adoption than any feature list.

Test with real users, then improve

Give the GPT or workflow to a small group of real users and watch them use it, without explaining it first. Note where they hesitate, what inputs they give that you did not expect, and where the output disappoints them. That observation is worth more than any amount of self-testing, because you already know how the product is supposed to work; they do not.

Sort feedback into three piles: fixes for things that are broken, improvements to things that work but could work better, and ideas for later. Fix the first pile before touching the others, and re-test after each round. A product improves through repeated small rounds, not through one large rebuild.

From GPT to a larger product

Some GPTs stay GPTs, and that is a fine outcome if the job is served. Others outgrow the format: they need to connect to other software, store user data, or handle many users at once. At that point, the GPT becomes a prototype and a specification: it proves the job is valuable, and its instructions and test cases become the brief for a built application.

Be honest about that step. A larger product needs development time, testing, and maintenance, and it should only be built when real users have shown the job matters. Avoid earnings or market claims you cannot support; a product's case rests on users who return, not on projections. Check current platform rules for publishing or sharing GPTs, since those change over time.

Frequently asked questions