How to Write Prompts for Vibe Coding
The Skill That Separates Good Builds from Great Ones
The quality of a vibe-coded product is determined almost entirely by the quality of the prompts that produced it. Vague prompts produce generic outputs. Specific, structured prompts produce production-quality results. This guide teaches you how to write prompts that consistently get you what you want — whether you are using Lovable, Bolt, Cursor, or Claude Code.
Talk to an ExpertWhat a good vibe coding prompt actually is
A good vibe coding prompt is a precise specification written in plain language. It tells the AI who the user is, what they need to do, what data is involved, and what the result should look like. It is not a request — 'make me a dashboard' — it is a brief: 'Build a dashboard for a marketing manager showing: weekly website visits (from a stored integer), campaign count (stored as an integer), and a list of recent campaigns (with name, status, and created date). Use a dark background with a lime green accent colour.' The difference between these two prompts is the difference between a generic template and a product that fits your specific use case. Every extra detail you include reduces the interpretation gap between what you imagined and what the AI builds.
A good prompt includes: user role, action needed, data involved, and desired visual style
Specificity reduces interpretation gap — the AI produces what you described, not what it guessed
Treat each prompt as a brief, not a request
Works across all tools: Lovable, Bolt, Cursor, Claude Code, Windsurf
Why prompt quality determines product quality
Most non-technical builders underestimate how much the prompting process affects the output. They assume the AI will figure out what they meant. It will not — it will produce the most plausible interpretation of what you wrote, which may be miles from what you intended. Poor prompts lead to iteration cycles that take longer than a traditional build would have. Good prompts get you to a working first version in one or two iterations — and every subsequent iteration is a targeted refinement rather than a complete rebuild. The time you invest in writing a better prompt saves three times that time in back-and-forth.
Vague prompts produce generic outputs that need complete rebuilds
Specific prompts get to a working version in one or two iterations
Time spent on prompt quality is repaid three-to-one in iteration time saved
The best vibe coders are the best at describing what they want precisely
How to write prompts that produce working products
Use this framework for every significant prompt you write. It takes two extra minutes and saves hours of iteration:
Structure: 'Build [feature] for [user type] that allows them to [action]. It should display [data fields]. Use [visual style/colours]. When [trigger event], it should [behaviour].'
Always specify the user: 'for a logged-in admin' is different from 'for any visitor'
Always specify the data: list the fields, their types, and where they come from
Always specify the visual style: colours, typography, layout orientation (grid vs list)
Break complex features into sequential prompts — one feature per conversation turn
End each prompt with a test criterion: 'This works correctly when [specific condition]'
Before and after: real prompt examples
The contrast between weak and strong prompts makes the principle concrete.
Weak: 'Add a user profile page.' Strong: 'Add a profile page for logged-in users showing: name, email, plan (from Supabase users table), and a button to cancel their subscription that triggers a Stripe cancellation.'
Weak: 'Make a form.' Strong: 'Build a contact form with fields: name (required), email (required, validated), message (required, minimum 20 characters). On submit, save to a Supabase contacts table and show a success message.'
Weak: 'Fix the bug.' Strong: 'When a user clicks the Delete button on a task card, the task disappears visually but returns on page refresh. Fix this so deletion persists to the Supabase tasks table.'
Claude Code and Cursor respond well to prompts that include the file path, function name, and exact behaviour expected
Common prompting mistakes
The mistakes that slow vibe coding builds most are almost always about prompt quality rather than tool limitations.
Combining multiple features in one prompt — producing a partial implementation of each
Not specifying the data model — the AI invents one that may conflict with your existing structure
Using vague visual terms like 'clean' or 'modern' instead of specific descriptions
Asking the AI to fix a bug without describing the exact expected behaviour when the fix is complete
Not including context from earlier in the conversation — AI tools lose context; re-state relevant details
How to become a consistently better prompter
Prompting is a skill, not a talent — it improves with deliberate practice. The best way to improve is to keep a log of prompts that produced excellent results and study the pattern. You will find they all include a specific user, a specific data structure, a specific visual style, and a specific success criterion. At Greta, our engineers write structured prompts as part of every AI-assisted build — it is a craft that we have invested in systematically.
Keep a prompt log — save every prompt that produced an excellent first-draft output
Study the pattern of your best prompts and apply it as a template
Always include: user, data, visual style, and success criterion
For complex features, write the prompt in a text editor first — review it before submitting
When builds need to be production-quality, Greta's engineers apply the same prompting discipline to every feature
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