How to Write AI Prompts That Actually Build Working MVP Features

The gap between “AI coding tools are amazing” and “AI coding tools keep giving me broken features” is usually not the tool — it’s the prompt. Founders who get consistently good results from vibe coding tend to prompt very differently from those who get frustrated and give up, and the difference is learnable.

Why Vague Prompts Produce Vague Code

“Build me a booking system” is technically a complete sentence, but it leaves dozens of decisions unmade: What happens if two people try to book the same slot? What information does the user need to provide? What does confirmation look like? An AI tool has to guess at all of this, and it guesses inconsistently — which is why the same rough prompt can produce a decent result one day and a broken one the next.

The fix isn’t a magic phrase. It’s giving the AI the same information you’d give a human developer if you were explaining the feature out loud.

The Elements of a Prompt That Actually Works

Describe the Specific User Journey, Not Just the Feature Name

Instead of “add a login,” describe: “A user enters their email and password. If correct, they’re taken to their dashboard. If incorrect, they see an error message and can try again. If they’ve forgotten their password, there’s a link to reset it via email.” This gives the AI a concrete path to follow instead of an open-ended concept to interpret.

State What Happens When Things Go Wrong

Most vague-prompt failures show up in the “unhappy path” — what happens on bad input, a failed network request, or an unexpected value. Explicitly stating “if the payment fails, show an error and don’t create the order” closes a gap the AI would otherwise fill with a guess.

Give It Constraints, Not Just Goals

“Keep this to a single page, no separate confirmation screen” or “use the existing button style already on the page” narrows the AI’s options in ways that produce more consistent, more usable output than an open-ended request.

Reference What Already Exists

If you’re adding to an existing app, tell the AI to match the existing patterns — the same data structure, the same visual style, the same naming conventions — rather than letting it introduce a new, inconsistent approach for each new feature.

Build in Small, Verifiable Steps

One of the most reliable techniques, especially for non-technical founders, is breaking a feature into pieces you can check one at a time:

1. Ask for the basic version of the feature working end-to-end, even without polish.
2. Test that it actually works the way you expect.
3. Ask for one refinement or edge case at a time, verifying each before moving to the next.

This is slower than asking for everything at once, but it catches problems while they’re small and isolated, instead of discovering three broken things tangled together after a single massive prompt.

A Before-and-After Example

Vague prompt	More effective prompt
“Add a way for users to leave reviews”	“Add a form where logged-in users can submit a 1-5 star rating and a text comment for a product. Show existing reviews below the form, newest first. If the user hasn’t purchased the product, hide the form and show a message instead.”
“Make the signup flow better”	“After signup, redirect the user to an onboarding page instead of directly to the dashboard. The onboarding page should ask for their company name and role before continuing.”
“Fix the bug with orders”	“When a user cancels an order that’s already been shipped, the app currently still marks it as cancelled without any warning. It should instead show a confirmation message explaining the order has shipped and cancellation isn’t available.”

Prompting Is a Skill, Not a Shortcut Around Review

Getting better at prompting will noticeably improve your results, but it doesn’t replace the need to verify what actually got built — especially for anything touching security, payments, or user data. How AI tools fit into a real MVP development workflow covers where this kind of AI-generated output still needs a human check, regardless of how well the prompt was written.

Handling a Bad Result Without Starting Over

When a generated feature doesn’t match what you wanted, resist the instinct to throw away the attempt and rewrite the whole prompt from scratch. Instead, point at the specific part that’s wrong: “the form submits correctly, but the success message disappears too quickly for users to read it — keep it visible until they navigate away.” This keeps everything that already worked intact and focuses the AI’s next attempt narrowly, which produces more reliable fixes than a fresh, broader prompt that risks changing things that were already correct.

If a second attempt at the same fix still misses, that’s often a sign the underlying instruction is ambiguous in a way you haven’t noticed yet — try describing the exact click-by-click sequence a user would follow, as if explaining it to someone who’s never seen the product before. That level of literalness feels excessive but reliably surfaces the gap in what was actually communicated.

Keeping a Personal Prompt Library

As you find phrasings that reliably produce good results for your specific product, save them somewhere you can reuse and adapt — a simple running document is enough. Founders who do this notice their prompting gets faster and more consistent within a few weeks, not because the AI tool changed, but because they stopped re-deriving the same clear-communication patterns from scratch every time.

Getting More Out of Whatever Tool You’re Using

These techniques apply across tools — whether you’re working in a chat-based builder or an editor-integrated assistant like Cursor or Copilot, specificity and small verifiable steps consistently outperform broad, one-shot requests. The tool matters less than how clearly you can describe what “working correctly” looks like before you ask for it.

Frequently Asked Questions

Why does my AI coding tool keep producing broken or incomplete features?

Usually because the prompt described an outcome too vaguely, leaving the AI to guess at details like edge cases, data validation, and error states — and it guesses inconsistently. More specific prompts produce more reliable results.

Should I write one big prompt or break it into smaller steps?

Smaller, sequential steps almost always work better for anything beyond a trivial feature. Ask for one piece, verify it works, then build the next piece on top of it, rather than asking for an entire complex feature in a single prompt.

Do I need to know technical terms to write good AI coding prompts?

Not extensively, but describing the specific behavior you want — what happens on success, what happens on failure, what the user sees at each step — matters more than technical vocabulary. Precision about behavior beats jargon.

How do I get an AI tool to fix a bug instead of introducing new ones?

Describe the exact steps to reproduce the problem and what you expected to happen instead, rather than just saying 'it's broken.' Vague bug reports lead to vague, sometimes unrelated fixes that can introduce new issues elsewhere.

Can better prompts eliminate the need for a developer to review the code?

No. Better prompts produce more reliable first drafts, but they don't replace understanding whether the underlying logic, security, and data handling are actually sound — that still benefits from a human review pass before real users depend on the feature.