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MODULE 2: HOW TO BUILD IT (WITHOUT CODE)
You've validated your idea. Now build it.
The prompt sweet spot: 200 words = perfect prompt length.
Under 100 words is too vague. Over 400 words means you're being too prescriptive and limiting the AI's ability to find better solutions.
You've probably heard about "one shotting" the ideal app. We're here to tell you it doesn't work and probably never will.
The 4-Element Prompt Structure
Every great prompt has these 4 elements in this order:
1. WHAT (should happen when users complete this flow)
Bad: "Add user authentication"
Good: "Now that we have a perfect personal dashboard, let's require users to signup/login to view it. Show a small teaser on why they should do this."
2. WHY (Context helps the AI make better decisions)
Bad: "Add a search feature"
Good: "Users need to quickly find specific exercises from a library of 500+. They often only remember partial names or want to filter by muscle group."
Now the AI knows to add autocomplete, partial matching, and filter options—without you specifying those features.
3. HOW (The user experience)
Describe the flow from the user's perspective, not technical implementation.
Bad: "Use a POST request to submit the form data to the API"
Good: "When users submit the form, show a loading spinner, then either show a success message and clear the form, or show validation errors next to each field"
4. WHEN (Important conditions or edge cases)
This prevents 90% of bugs.
Bad: "Add payment processing"
Good: "Add Stripe payments. If payment succeeds, unlock the pro features and append a Pro badge to their profile page. If it fails, keep them on the free plan and show them the error message from Stripe. If they're already a paying customer, don't let them pay again—show them their account page."
You've prevented three common bugs: failed payments, duplicate charges, and missing confirmation emails.
Real Example: Habit Tracker App
Here's a complete prompt that works:
"I need a habit tracking app that helps users build daily habits through streak tracking and social accountability.
Users will add habits they want to build (like 'Meditate 10 minutes,' 'Read 30 minutes'), check in each day when they complete a habit, see their current streak and history, and share their streaks with friends for accountability.
The flow should be: sign up with email/password, add their first 1-3 habits on an onboarding screen, see a clean home screen with today's habits (checkboxes plus current streak count), check off habits throughout the day, see a calendar view of their history.
Important edge cases: if they miss a day, the streak resets to 0 (be clear about this). They should be able to check off a habit only once per day. If they check off all habits for the day, show a celebration animation. Let them edit or delete habits, but keep the history.
Success looks like: a user can add a habit, check it off daily, and see their streak grow over time.
Design: clean, minimal, mobile-first. Use encouraging language."
Why this works:
- Clear outcome
- User context
- Specific flow
- Edge cases covered
- Success criteria defined
- Design guidance without being prescriptive
Common Mistakes
1. Asking for too much at once
Bad: "Build me a complete fitness app with workout tracking, meal planning, gym capacity warnings, social features, progress photos, and a marketplace to buy meal plans from coaches."
That's 5 separate apps. The AI will build something that does a little bit of everything, poorly.
Fix: "Build me a workout tracking app. I'll add other features later, but start with just tracking exercises, sets, and reps."
2. Not testing on actual mobile devices
Desktop browser doesn't equal actual phone. Touch targets, scrolling, keyboard behavior, screen sizes—all different.
Fix: Use Anything's instant preview on your actual phone. Test every feature on a real device.
3. Not iterating
Test every feature you add, check for consistency. Review flows for users end to end, do not race to just see it "done."
4. Not asking for feedback
Share your work often with friends and family, get input, refine, polish. See your app through the eyes of others to understand and remove assumptions, confusion and UI inconsistencies.
5. Moving too fast
AI models have incredible capabilities for research, which is why we include a mode for Discussion (read only, no edits allowed). Query the AI on your build to date, discuss an API you're considering, ask about pitfalls or issues, improvements or design features. Consider the AI a partner, not just your coding bot.
6. Not backing things up
Get comfortable with the version history system in your solution of choice, know how it works and what it can offer you. If possible, duplicate and copy your projects for storage.
7. Not using images
AI models love to look at images. Share images for design inspiration or, in testing, share snapshots of your Chrome Dev Console or logs so the AI Agent can debug more effectively.
8. Not understanding your sources
Agentic building can seem like wizardry but it can also provide you with demo data or mock information that you need to replace from a reputable source. Looking to scrape news, stocks, crypto or other dataset? Know your data vendors and their APIs or ask the AI agent for input on the best sources.
What's Next?
✅ Built your MVP? → Module 3: Price It Like You Mean It →
📌 Other modules in this series:
