Writing Effective Prompts
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Writing Effective Prompts
The quality of your project description sets a ceiling on everything VibeMap generates from it. · Recommended
This page is for the blank-start path — when you describe your product from scratch rather than starting from a template. A well-crafted prompt produces focused personas, relevant features, and a realistic schema; a vague one produces generic output you'll spend longer editing than you saved.
What makes a good prompt
Be specific about your users
Tell the AI exactly who uses the product. The more specific you are, the sharper the personas and user stories.
- Vague: "People who want to manage tasks."
- Specific: "Freelance designers managing client projects, deliverables, and invoicing."
Describe the features you want
List the key things users should be able to do — focus on capabilities and outcomes, not every screen.
- Vague: "Users can track their progress."
- Specific: "Users set weekly goals, log completed tasks against them, and see a dashboard of progress trends over the past 30 days."
Mention your tech stack (optional)
Stack preferences improve the schema, page structure, and component suggestions.
- "Built with Next.js and Supabase."
- "React Native mobile app with a Node.js backend."
- "WordPress plugin with a custom admin panel."
Define the scope
Help the AI understand what's in and out for this version.
- "MVP focused on core task management — no team features in v1."
- "Full platform including admin dashboard, reporting, and integrations."
Include business context
A sentence about the market or business model sharpens the AI's decisions.
- "Competing with Notion and Linear for small development teams."
- "Subscription SaaS targeting agencies with 5–20 employees."
The anatomy of a great prompt
A strong prompt covers these five elements in 2–4 paragraphs:
- What it is — one sentence describing the product.
- Who it's for — the target users and their context.
- Core features — 3–8 key capabilities.
- Technical context — stack, platform, or constraints.
- Scope boundaries — what's included and excluded.
Worked examples
Good — a recipe platform
A recipe-sharing platform for home cooks who want to discover, save, and share recipes with friends and family. Users create detailed recipe pages with ingredients, step-by-step instructions, photos, and cooking times. They can follow other cooks, save recipes to personal collections, and leave reviews with star ratings.
The app includes search with filters for dietary restrictions (vegan, gluten-free, etc.), cuisine type, cooking time, and difficulty. Users get a personalised feed based on who they follow and the cuisines they engage with.
Built with Next.js and PostgreSQL. MVP scope — no meal planning, grocery lists, or video in v1.
Why it works: specific users, clear features, defined stack, explicit scope boundaries.
Bad — the same idea, underspecified
A cooking app where people can share recipes and find new ones to try.
Why it fails: no detail on users, features, tech, or scope — the AI guesses at everything and produces generic output.
Good — an internal tool
An internal tool for a property-management company (50–200 units) to track maintenance requests from tenants. Tenants submit requests through a simple form describing the issue and attaching photos. Property managers see a dashboard of all open requests, assign them to maintenance staff, set priority, and track resolution.
Maintenance staff get a mobile-friendly view of their assigned tasks with directions to the property. Managers get weekly reports on response times, resolution rates, and recurring issues by property.
React frontend with a Node.js API and PostgreSQL. Must integrate with Twilio for SMS notifications when a request's status changes.
Why it works: distinct roles (tenant, manager, staff), specific workflows, a named integration, and business context.
Bad — the same idea, underspecified
Build a maintenance tracker for property managers with notifications and reporting.
Why it fails: no user types, no workflows, and no definition of what "notifications" and "reporting" actually mean.
Common mistakes to avoid
⚠️ Watch out: These are the patterns that most often produce disappointing first generations.
- Too short — one-sentence prompts force the AI to assume too much.
- Too abstract — goals without concrete features ("improve engagement").
- Feature soup — 30 features listed with no context or priority.
- Copied marketing copy — promotional language doesn't tell the AI what to build.
- Assuming context — spell out industry jargon; the AI doesn't know your domain shorthand.
Prompt length guide
| Length | Typical result |
|---|---|
| 1–2 sentences | Too vague — generic output |
| 1 paragraph (3–5 sentences) | Acceptable for simple products |
| 2–4 paragraphs | Ideal — enough detail for high-quality results |
| 5+ paragraphs | Fine if well-organised, but watch for contradictions |
The sweet spot is 2–4 focused paragraphs covering what the product does, who it's for, its key features, and any technical constraints.
⚡ Power-user hints
- Iterate, don't perfect. Start from a solid base prompt, review the generated project summary, and let the conversational agent add the features or personas you missed — rather than rewriting the prompt and starting over.
- The summary is your feedback loop. If the AI misread your intent, the gap is usually a thin spot in your prompt. Add detail there and regenerate.
- Front-load constraints. State scope and stack early in the prompt; the AI weights the opening more heavily when framing the whole spec.
↔ The traditional way
A discovery brief or PRD template gets a team to the same set of questions — users, features, scope, constraints — but takes days of interviews and drafting to fill in. Here, the same structure is a single prompt: answer the five elements above and the AI expands them into the full spec, then you refine in place instead of circulating document revisions.
What's next
- Creating your first project — put your prompt to work.
- Project summary — how VibeMap interprets your prompt, and how to correct it.