How to Write AI Prompts That Actually Build Working Apps

TLDR

• Give context first. Say who the app is for, what stack to use, and what the data looks like before you ask for a single feature.
• One thing per prompt. Ask for a whole app in one message and you get a messy, half-finished one. Build it in slices.
• Describe what 'done' looks like. List the outcome and the edge cases, not just the happy path.
• Give it a way to check its own work. Tests, a screenshot to match, an error to fix. The AI can then loop until it passes.
• When it breaks, don't re-run the same prompt. Paste the exact error, point at the file, and fix the root cause. Steal the template at the bottom.

Why most prompts get you 80% of a broken app

AI app builders like Lovable, Cursor, and Claude Code will happily turn a one-line prompt into something that runs. The problem is the gap between runs and works. Type "build me a CRM" and the tool has to guess a hundred decisions you never made: who uses it, what a record looks like, what happens when a form is empty, what the buttons should say. It guesses, you correct, it guesses again, and an hour later you have a pile of half-right screens.

The builders that ship treat a prompt like a brief for a contractor, not a wish. You are not being clever. You are removing the guesses. Below are the five ingredients that do that, each with a bad prompt and the specific version that actually builds the thing.

1. Load the context before you ask for anything

Every AI tool starts a fresh conversation with zero memory of your project. As Supabase puts it, "context accumulates throughout your coding session, but AI assistants usually start fresh with each new conversation." So the first prompt has to carry the load: who the app is for, the stack, and the shape of the data.

Most builders (Lovable, Cursor, Base44) have a knowledge base or rules file where this context lives permanently. Fill it out once. Treat it like a lightweight product brief: what you are building and why, who uses it, the must-haves versus the nice-to-haves, and the design direction. Claude Code reads a `CLAUDE.md` file at the start of every session for exactly this reason.

Bad: "Build a login page." → Good: "Create a login form in React using Tailwind, connected to Supabase Auth, with error handling for expired tokens and social login options."

The second one names the framework, the styling, the backend, and one edge case. Supabase found that this three-layer shape (technical context, then what the feature does, then how it connects and what can go wrong) means "you receive functionality that's much closer to your requirements on the first try."

2. Be specific about data, design, and words

"Clean and professional" means nothing to a model. "Deep navy and white, rounded cards, generous whitespace, similar to Linear's interface" gives it an actual target. Same with data: listing the fields up front is what lets the tool build the right database table the first time instead of a generic one you rebuild later.

The table below is the checklist. Everything in the vague column forces the AI to guess. Everything in the specific column is a decision you already made.

One trick from Lovable's own docs: end your prompt with "Ask me any questions you need in order to fully understand what I want." The tool comes back with focused questions and fills its own gaps before writing a line of code. Claude Code has the same move: tell it to interview you first and write the answers to a spec file.

3. Build in slices, not in one giant prompt

The single most common mistake is stuffing an entire app into one prompt. Lovable warns against it directly: overload the AI with a long list of commands and "it may struggle to follow through with every action, leading to a messy, unfinished app." The fix is to prompt by component, not by page, and by feature, not by app.

Ship the auth flow. Confirm it works. Then the dashboard. Then the settings page. Each slice is small enough that the model holds all of it in its head, and small enough that when something breaks you know exactly where. Claude Code's guidance is the same: "Keep tasks small: one feature slice, one refactor, one upgrade, then reset." For anything non-trivial, ask for a plan first and approve it before any code gets written. Plans catch wrong assumptions early, before they turn into churn.

This 17-minute walkthrough shows the slice-by-slice rhythm end to end in Lovable, from first prompt to a working app.

4. Describe the outcome and the edge cases

The happy path is the easy 80%. The 20% that makes an app feel broken is everything else: the empty state, the expired session, the network error, the field left blank. If you do not name these, the AI ships the demo and skips the reality.

You do not have to think of them all yourself. After the first draft, ask the tool to find them for you. Supabase recommends two follow-ups that surface most of the gaps:

• "What could go wrong with this code? What edge cases should I handle?"
• "Review this code as if it's going live tomorrow. Identify security concerns, performance bottlenecks, and missing error handling."

Better still, give the AI a way to check its own work so it does not need you as the error detector. Claude Code's core rule is to hand it a pass-or-fail signal: a test, a build, or a screenshot to compare against. Then it runs the check, reads the result, and iterates until it passes. Instead of "implement a function that validates emails," write: "write a validateEmail function. Example cases: user@example.com is true, invalid is false, user@.com is false. Run the tests after implementing."

5. When it breaks, change the prompt, not the volume

Well-structured prompts rarely produce perfect code on the first try, and that is normal. The power is in the iteration loop. But re-sending "it's still broken" three times just pollutes the conversation with failed attempts and makes the AI dumber. Each retry should carry new information: the exact error, the file to look in, and what fixed looks like.

That last row matters more than it looks. Claude Code's own docs call it out: after two failed corrections, the context is full of wrong turns, so clear it and write a better opening prompt that bakes in what you just learned. A clean session with a sharp prompt almost always beats a long one dragging its mistakes behind it.

The reusable template (copy this)

Paste this into any AI builder, fill the brackets, and delete the lines you do not need. It covers every ingredient above in the order the model wants them.

GOAL: Build [feature] for [who uses it and what they're trying to do].

STACK: [React / Next.js], [Tailwind], [Supabase / your backend]. Match the patterns already in this project.

DATA: [Each X has these fields: ...]. [How records relate.]

BEHAVIOR: The user can [action 1], [action 2], [action 3].

DESIGN: [Colors], [layout], [a reference app or screenshot].

EDGE CASES: Handle [empty state], [errors], [loading], [permissions].

DONE LOOKS LIKE: [The exact thing I should be able to do when it works.]

Before you code, ask me any questions you need, then give me a short plan. After building, run the checks and show me the result.

It looks like a lot. It is 90 seconds of typing that saves an hour of correcting. And once it lives in your knowledge base or rules file, most of it is filled in for every prompt after the first.

Bottom line

The people getting working apps out of AI builders are not writing longer prompts. They are writing prompts that remove guesses: context up front, one slice at a time, the outcome and edge cases spelled out, and a check the AI can run itself. Do that and the tool stops handing you 80% of a broken app and starts handing you a working one you can actually ship.

Sources worth reading in full: Claude Code best practices, Lovable's prompting docs, Supabase's guide to prompting, and the Lovable Prompting Bible.