How to write AI prompts to build an app

What is a build prompt, and why write one before any code?

A build prompt is the written brief you give an AI coding tool so it can start building an app. It should explain the job before asking for code. An AI coding tool cannot infer which vague feature matters most to you, which information is private, or which existing files it must preserve. Make those decisions explicit enough that you can recognize a correct result.

The same brief can be adapted for tools such as Claude Code, Cursor, or Lovable. Their interfaces and capabilities differ, so treat the text as a specification rather than a promise that one prompt will produce a production-ready app. Review the tool’s plan and inspect the result at each step.

Whatever you call it, a build prompt, an app prompt or the step from idea to prompt to app, it is text that turns an idea into something a coding tool can act on. The quality of the app tracks the quality of that text more closely than it tracks which tool you pick.

What should AI prompts for app development include?

Describe a specific user, the situation that brings them to the app, and the outcome they need. Then give the smallest complete workflow. A list of nouns such as ‘dashboard, AI, analytics’ leaves the tool to invent the important behavior.

State your technical constraints only when they are real. In an existing repository, ask the tool to inspect the code and follow its conventions. In a new project, explain hosting or device requirements rather than choosing a large stack by default. Mark undecided details as questions instead of letting assumptions look like requirements.

• User and problem: who is doing what, and why the current method fails.
• Workflow: the actions and results needed for the first useful version.
• Data and access: what is stored and who may read, edit, or delete it.
• Boundaries: excluded features, permitted services, and cost limits.
• Quality checks: success, empty, invalid, and failure cases you will test.

What is a reusable template for a build prompt?

Fill in the brackets with concrete details. Leave out credentials, personal records, and private customer data; use fictional examples to explain a format. Ask the tool to identify blocking questions before implementing a guess that would be costly to undo.

Build a first version of [app].

User: [specific audience].
Problem: [current workflow and observable pain].
Success: [one result the user can achieve].

Core flow:
1. [Input or starting action]
2. [Main operation]
3. [Result, save, or export]

Data and access: [fields, persistence, permissions].
Constraints: [existing stack, devices, allowed integrations].
Do not include yet: [excluded features].
Failure states: [invalid input, network failure, unavailable service].

Acceptance checks:
- [Concrete happy-path check]
- [Persistence or permission check]
- [Concrete failure-path check]

First inspect the project, list assumptions and blocking questions, and propose a small implementation plan. Build and verify one complete flow before adding optional features. Explain what was tested and what still needs manual verification.

If filling in the template feels hard, the problem is usually upstream: the scope is not decided. How to scope an MVP produces most of the fields you need, and the AI coding prompt builder will assemble the template from your answers.

How do you replace vague requests with observable behavior?

Instead of ‘build an AI feedback manager’, try: ‘A freelance designer pastes client feedback as plain text. Split it into editable items with an owner and an open/done status. Show unresolved items and export them as text. Preserve edits after a reload. If a parsing service fails, keep the original text and let the user create items manually.’

That version specifies an input, an output, persistence, and a fallback. It still leaves an important question: whether feedback can be sent to an external service. Resolve that before connecting one. The prompt should expose that decision, not disguise it as a technical detail.

A useful habit is to read each sentence of your prompt and ask what a reviewer could check. ‘Easy to use’ cannot be checked. ‘A new user can create a request in two taps from the empty screen’ can. Rewrite until each line names something you can see, click or reload.

What does a complete AI prompt to build a small app look like?

Here is the template filled in for a real-sized first version. It names the user, the job, what to store, what to leave out, and how to check the result. The example is intentionally small enough to review. Replace the fictional details with what you learned from actual users before building your own app.

Build a first version of a client-feedback tracker for one freelance designer.

User: A designer who receives feedback in email and chat.
Problem: Requests get lost between messages and final handoff.
Success: The designer can see every open request for one project and mark it done.

Core flow:
1. Create a project and paste feedback as plain text.
2. Turn each line into an editable request with a short title and open/done status.
3. Show open requests first; let the designer edit, add, and complete them.

Data and access: Store projects and requests locally in the browser for this first version. Do not send pasted client text to an external AI service. State this limitation in the UI.
Constraints: Mobile-friendly web app. No login, billing, team roles, or analytics yet.
Failure states: Empty feedback, very long lines, duplicate requests, and unavailable browser storage. Keep the original text if parsing fails.

Acceptance checks:
- Three pasted lines create three editable requests.
- Completing one request keeps it done after reload.
- If storage fails, the user sees an error and can copy their text; no input disappears.

Before coding, list any blocking questions and the smallest implementation plan. Build the complete core flow, run the checks above, and explain what was tested.

Paste the brief into your AI coding tool, then answer its blocking questions and review the proposed plan. Give it access only to the project and services you intend to use. If you already have a repository, add a first line asking it to inspect that repository and follow its conventions.

What does the build prompt on Post your build ideas contain?

Every idea on the board has a generated build prompt. It is built by a fixed template from the idea’s fields, not written by a language model, so the same idea always produces the same text. It starts with ‘Build’ followed by the idea’s title, then ‘Context:’ with the full description exactly as the author wrote it.

After the context it adds a short list of requirements: ship the smallest version that delivers the core value, pick a stack that can be deployed in a day and state the choice before writing code, and handle the empty state, the error state and the offline case. If the author included a pricing idea, that appears as a line to build toward. It ends by asking the tool to list the screens first and build them one at a time.

That makes the generated prompt a starting point, and its quality depends on the idea’s description. A long, specific description produces a useful prompt; a one-line idea produces a short one. It does not include acceptance checks, data and access rules or an exclusion list, so those are the first things to add using the template above.

On an idea page you can copy the prompt, or open it directly in Claude Code, Codex or Cursor. You can also paste the same text into Lovable or any other builder. To see real examples, browse the idea board. If you have an idea of your own, post it; its page gets a prompt built from your description.

How do you go from an idea to a prompt to a working app?

1. Check that the idea names a user and a problem. If it does not, find a better-defined idea or talk to a person who has it.
2. Cut it to one outcome with an explicit not-in-this-version list.
3. Copy the generated prompt, or write one from the template, and add data rules and acceptance checks.
4. Paste it into your coding tool and ask for assumptions, blocking questions and a plan before any code.
5. Answer the questions, approve a plan that covers one flow, and let the tool build that flow only.
6. Run your acceptance checks yourself, then add the next flow in a new prompt.

Splitting the work into flows matters because errors compound. A tool that builds five screens from one prompt may produce five screens that disagree about the data. A tool that builds one flow, gets it checked, and then builds the next has less to untangle. For the full process in an agent, see how to build an app with an AI coding agent, and for the Claude Code specifics, how to build an app with Claude Code.

How do you check what the tool built?

Do not assume the idea’s author has already validated demand or resolved every implementation question. After the tool builds something, run the acceptance checks yourself. Try an empty input, a failed save, a reload, and the smallest supported screen. If the app has private records, test access with separate users. Only claim a behavior works when you have evidence from the running app.

Keep a short list of what the tool said it tested against what you tested. Gaps between the two are where problems hide. If an answer is vague, such as ‘it should work’, ask for the command or manual steps that show it.

If the checks keep failing in the same place, the prompt is probably missing a rule rather than the tool being wrong. Add the rule to the prompt, rerun the one flow, and keep the corrected text for next time.