Play 2: Write User Stories That Work

Without user stories, you'll end up asking the AI to "build a dashboard" or "add a login button." The AI will happily comply, but the features won't be connected to a real user need. You'll get a collection of disconnected parts, not a coherent product.

User stories are the bridge between your high-level vision and the low-level implementation details. They force you to think from the user's perspective, which is a critical counter-balance to the AI's purely technical focus. For UpvoteVibe, every feature started as a user story.

This play shows you how to create simple, powerful user stories that will keep your AI collaborator focused on what really matters: the user's success.

The Play-by-Play: From Personas to Journeys

I worked with ChatGPT to turn the high-level personas from the product vision into detailed, actionable stories.

Step 1: Identify Your Players (5 mins)

Look at your product vision and identify the key actors. Who is this product for? For UpvoteVibe, it was clear from the vision document that we had three main players for the MVP:

1. The Maker: The person submitting a project.
2. The Explorer: The person discovering new projects.
3. The Community Member: The person giving vibes.

Step 2: Map Their Journeys (20 mins)

For each player, map out their complete journey. What are they trying to achieve? What are the key steps? I used the AI as a partner to think through the emotional and logistical beats of each journey.

My prompt was:

"Let's map the full journey for a Maker, from the moment they land on UpvoteVibe to the moment they've successfully shared their project. What are the key steps, potential friction points, and success moments?"

This process led directly to the five core user stories that defined the scope of UpvoteVibe:

• maker-shares-project.md
• explorer-discovers-projects.md
• community-member-gives-vibes.md
• maker-monitors-response.md
• newcomer-learns-methodology.md

Step 3: Define the Wins (5 mins)

For each story, define what success looks like in concrete, measurable terms. This is how you'll know if the features the AI builds are actually working.

For the maker-shares-project story, the success criteria weren't just "the form works." They were:

• Success Metric: >80% of users who start the submission form complete it.
• User Outcome: The maker feels proud and confident sharing their work.
• System Behavior: The project appears on the site immediately after submission.

These "wins" become the acceptance criteria for the AI's work. You can literally put them in the prompt: "The feature is complete when a user can submit the form and see their project live on the homepage, and the form has a completion rate of over 80% in testing."

How User Stories Guide Your Prompts

Once you have these stories, your AI prompts become much more powerful.

Instead of:

"Build a project submission form."

You can now write:

"Implement the maker-shares-project user story. The goal is to create a low-friction, high-honesty submission process. Refer to the success criteria in the story doc. The user should feel confident and proud. Here is the data model..."

This is the difference between asking for a pile of bricks and asking for a wall. The user story provides the architectural plan that guides the AI's work.

With your user stories in hand, you're ready to start thinking about the look and feel of your app.

Next: A Pragmatic Design System