What a user story tells the AI
A user story is one short sentence that explains who needs the feature, what they need to do and why it matters.
I use user stories to stop an AI build from becoming a collection of screens. The story keeps the feature tied to a person and a useful result.
Step 1: name the person clearly
Avoid writing as a user. A client, studio owner and administrator have different goals and should not see the same information.
For our design portal example, the first person is a client reviewing a design. That is specific enough to make the next choices clearer.
Step 2: describe the result, not the screen
A weak story says: As a client, I want a dashboard. It tells the AI what to draw but does not explain the job.
A stronger version says: As a client, I want to review the current design and leave feedback, so I can approve the next step without sending a long email.
The dashboard may be part of the answer. It is not the user need.
Step 3: add acceptance checks
The story explains why the feature exists. Acceptance criteria explain what must work before you call it complete.
- The client can open the correct design from a shared link.
- The client can approve it or leave a comment.
- A blank comment is not submitted.
- The client sees when the response has been saved.
- Editing the design is outside this version.
Step 4: turn one story into small tasks and tests
Split the story into work the AI can complete and you can check. Shared link access, displaying the design, saving feedback and showing confirmation can be separate tasks.
Ask for tests that prove the rules in each task. Then complete the journey yourself to check the parts a code test cannot judge, such as whether the next step is clear.
I keep the story with the feature, PRD and Kanban tasks in CodeSpring. A new coding session can read the reason behind the task instead of receiving an isolated instruction.