Plan an App with AI: PRD, MVP Scope, and Acceptance Checks
“Build me an app” can produce attractive screens without answering who will use them, which problem matters, or where the first version should stop. A short requirements document gives the AI something concrete to work toward. This guide uses a reading-list app to turn an idea into a focused first version, an implementation sequence, and acceptance checks.
Start with a person, a situation, and a problem
Set tool selection aside and answer: Who is trying to do what, in which situation, and what gets in the way today?
For example: someone receives book recommendations from friends and wants to save the titles quickly. Their notes are scattered across chats and scraps of paper, so they cannot find them at the bookstore. That gives you a clearer starting point than a reading app with AI recommendations, social features, and rankings.
This problem is an example hypothesis, not a research finding. Ask potential users about the last recommendation they saved, whether they found it later, and what was inconvenient. Recent experiences are more useful than asking whether they would use an imaginary app. The GOV.UK guide to understanding user needs also starts with users’ tasks and distinguishes needs from a chosen solution.
Give the AI a brief such as:
User: someone who accumulates books they want to read.
Situation: save a recommendation now and retrieve it when choosing a book.
Question to investigate: is one reading list easier to keep using than their current notes?
Decide what you want to learn before deciding how many features to build.
Keep the MVP to one complete journey
MVP means minimum viable product. For this exercise, the first version lets someone add a book, find it later, and mark it as read. It gives us a way to investigate the core use case; completing it does not demonstrate willingness to pay.
| Include in the first version | Why it matters |
|---|---|
| A required title and optional author | Capture a recommendation. |
| To-read and read states | Choose the next book. |
| Editing, deletion, and local storage | Correct mistakes and keep records after reopening. |
Put accounts, cloud sync, AI recommendations, sharing, and subscription payments in a later list. Each brings more screens, data rules, and tests. Finish the main journey, then use feedback to choose an extension.
Keep an unresolved list too. Are duplicate titles allowed? Must data follow the user to a different device? Is this a browser app or an installed phone app? AI can explain the tradeoffs, but the decision needs to serve your goal.
Plan empty states and failures alongside the happy path
The main journey is open the list → add a title → save → return to the list → mark as read. Sketch each step on paper or describe what appears and what the user can do. Polished UI can wait.
| Situation | Agreed behavior |
|---|---|
| No books yet | Explain the purpose and show an add button. |
| A title contains only spaces | Keep the form open and explain that a title is required. |
| The same title is added again | Allow it in this example, with a separate identifier. |
| Saving fails | Show an error and keep the input; do not report success. |
| A book is deleted | Ask for confirmation, then remove only that entry. |
Write these decisions down so the AI does not silently invent product rules. This example stores data locally; cross-device sync and backups are outside its first-version scope. If moving between devices is the user’s main need, revisit that choice.
A one-page PRD you can keep in the project
PRD stands for Product Requirements Document. A small project can start with a concise record of the important decisions and update it when those decisions change.
Download the blank app planning template, or use this completed example:
Goal and user: help someone save book recommendations and find them later.
First-version journey: add title → save → browse list → mark as read.
Required features: required title, optional author, reading status, editing, confirmed deletion, local storage.
Out of scope: accounts, sync, AI recommendations, social features, payments.
Screens and states: list, add/edit form, empty list, invalid input, save failure.
Data rules: duplicate titles allowed; separate identifiers; reload saved records on reopening.
Unresolved: target platform and devices, storage approach, whether export is needed.
Completion evidence: acceptance-check results and specific feedback from people trying the app.
Label missing decisions unresolved. A polished document should not turn an AI’s guesses into approved requirements.
Write acceptance checks as observable outcomes
“Looks good” and “works correctly” do not tell you what to test. Write an action and its expected result. GOV.UK’s user-story guidance describes acceptance criteria as outcomes used to check whether a need has been met. These checks are our own examples for the reading-list app.
| ID | Action | Expected result |
|---|---|---|
| A1 | Enter a title and save | One entry appears, initially marked to-read. |
| A2 | Enter only spaces as the title | No entry is added; the form explains the requirement. |
| A3 | Add a book, close, and reopen | The record remains in the agreed platform environment. |
| A4 | Mark a book as read, edit its author, and reopen | Both changes remain. |
| A5 | Cancel deletion, then confirm it | Cancel changes nothing; confirm removes only that entry. |
| A6 | Simulate a storage failure in a test | Show an error, retain the input, and do not report success. |
These are targets for implementation and testing, not results from an app built for this article. A successful build does not replace running A1 through A6.
Ask AI to turn the brief into deliverable tasks
You can give the AI this prompt with the PRD:
Read this app plan. Separate confirmed requirements, questions that would change the first-version scope, and assumptions you propose. Do not treat assumptions as decisions. Ask only the key questions needed to resolve gaps. Then produce a one-page PRD, screen and data states, and implementation steps linked to acceptance checks. Complete the planning first; do not generate the entire app at this stage.
Start with the list and form, then add creation and storage, followed by status changes, editing, and deletion. Finish the failure paths and acceptance checks. Each task should have a result you can demonstrate, such as adding a book and finding it after reopening, rather than a broad label like “finish the frontend.”
If the first task already includes accounts, payments, recommendations, and a database, revisit the scope. Once requirements are clear, read the AI coding tool comparison. For a workflow that connects planning to testing and delivery, continue with the Agentic SDLC guide.
Five checks before starting implementation
- You can describe the user, situation, and problem in one sentence.
- The first version has a complete journey and an explicit out-of-scope list.
- Empty states, invalid input, and save failures have agreed responses.
- Every core feature has an actionable acceptance check.
- Open questions, task order, and the feedback you want to gather are recorded.
Save the brief and checks in the project, then begin with the first small task. Find the next development, testing, and launch guides in the AI development map.
