Create AI Products with Claude

Validate the problem first

An AI product is a solution to a problem someone already feels. Before building, talk to five to ten people who have that problem. Ask how they solve it today, what it costs them in time or money, and what they would pay to solve it better. If nobody feels the problem strongly, no amount of AI will create demand. Write down what you heard in their words, not yours, and use those words in the product design.

Ask Claude to help you turn those conversations into a problem statement: who has the problem, what they do today, and what a better outcome looks like. If the statement stays vague after that exercise, the idea needs more user conversations, not more building.

Define the smallest useful version

The smallest useful version does one job well for one kind of user. A tool that turns meeting notes into action lists is a product. A platform that does notes, scheduling, analytics, and reporting is a project that may never launch. List every feature you imagine, then keep only the ones needed for the core job. The rest go on a later list, and most of them will never be needed.

Write the product as a simple flow: the user provides an input, the product does one clear thing, the user gets an output they can check and use. If any step in that flow is unclear, the product is not ready to build yet.

Design the limits, not just the features

AI products need stated limits. What inputs does the product accept? What will it refuse or flag? What happens when the answer is uncertain? How does a user correct a wrong output? Answer these before building, because they shape the whole design. A product that says when it is unsure is more trustworthy than one that always sounds certain.

Also decide the data rules early: what user data the product stores, where it is stored, and how a user deletes it. These are not details to add later. They affect the architecture, the cost, and whether businesses can adopt the product at all. If you are unsure, get advice from a developer or a lawyer before launch, especially for health, finance, or personal data.

Build a prototype and test it yourself

Ask Claude to help build a prototype of the core flow: an interface, a prompt or workflow behind it, and sample outputs. Use it yourself with real inputs, including awkward ones: missing information, unusual spellings, very long inputs, and contradictory requests. Note every failure. A prototype that only works on perfect inputs is not a prototype, it is a demo.

Fix the failures in order of how often a real user would hit them. A failure that happens on every third use matters more than a failure on an edge case nobody will meet.

Test with real users and listen

Give the prototype to a small group of the people you interviewed, watch them use it without guiding them, and record what they actually do, not what they say they would do. Where do they hesitate? What do they misunderstand? What do they try that the product does not support? That observation list is your next build list. After the test, decide honestly: keep and improve, change direction, or stop. All three are legitimate outcomes, and stopping early is cheaper than launching something nobody wants.

Help me define the smallest useful version of an AI product.
Problem: [the problem, in the users' words]
Current workaround: [how users solve it today]
Users: [who they are]

Give me:
1. A one-sentence product description.
2. The single core job it must do well.
3. The input the user provides and the output they receive.
4. Three features to include now, and three to leave for later.
5. Three ways it could fail, and what the product should do in each case.
Rules:
- Do not add features beyond the core job.
- If any part is unclear from my notes, say so and ask one question.

Frequently asked questions