Turn rough ideas into build-ready product requirements in minutes.
PRDForge is an AI product planning tool for founders, product managers, and indie builders who need to turn a raw idea into a structured PRD they can actually build from. It guides users through clarifying scope, users, goals, requirements, and launch milestones, then outputs an editable, shareable document and a coding-agent prompt.
Founder Alex, 31 - Alex has a startup idea and needs to move quickly from concept to something an engineer can estimate. He wants a clear PRD without spending days writing one.
Product Manager Priya, 39 - Priya is responsible for shaping features for an existing product. She needs a faster way to capture scope, requirements, and success metrics in a consistent format.
Indie Builder Sam, 27 - Sam builds products solo and relies on AI tools to move from idea to code. He needs a practical, implementation-oriented spec that reduces planning friction.
Alex has a rough idea for a tool that helps freelancers manage invoices, but he has no time to write a formal spec. He drops the idea into PRDForge and answers a few guided questions about audience, workflow, and success criteria.
Within minutes, Alex has a structured PRD with goals, personas, requirements, and milestones. He exports a coding-agent prompt, shares the draft with a cofounder, and uses the document to get faster feedback and a cleaner build plan.
Instead of starting from a blank page, Alex starts from a high-quality product brief that reduces ambiguity and accelerates implementation. That saves planning time, improves alignment, and increases the odds of shipping the right product on the first attempt.
Team & resourcing - Small team - 2 engineers, 1 product designer, part-time PM, and shared QA/ops support.
Paste this into Cursor, Bolt, Lovable, or v0 to start building.
Build a web app called PRDForge that turns a rough product idea into a build-ready PRD and a coding-agent prompt. Core product: Users paste a rough idea, answer adaptive clarifying questions, and receive a structured PRD with sections for summary, goals, personas, user stories, functional requirements, UX flow, narrative, success metrics, technical considerations, and milestones. They can edit each section, regenerate one section at a time, export to PDF/Markdown/text, and copy a prompt for tools like Cursor, Bolt, Lovable, or v0. Default tech stack: Next.js 14 with App Router, TypeScript, Tailwind CSS, shadcn/ui, PostgreSQL with Prisma, Redis for background jobs, Auth.js or Clerk for auth, Stripe for billing, PostHog for analytics, OpenAI or Anthropic for generation, and S3-compatible storage for exports. Primary screens and flows: 1. Landing page with product explanation, sample output, and CTA to start a PRD. 2. Idea intake screen with a single text area and template picker. 3. Adaptive clarification flow that asks 3-7 questions based on missing context. 4. PRD editor with collapsible sections, autosave, version history, and regenerate-section actions. 5. Prompt export modal with concise and detailed prompt variants. 6. Share page with read-only access, comments, and export options. 7. Billing/settings page with plan usage, workspace settings, and deletion controls. Data model: User(id, email, name, plan, createdAt), Workspace(id, ownerId, name), Project(id, workspaceId, title, rawIdea, status, templateType, createdAt), ClarificationQuestion(id, projectId, question, answer, confidence), PRDSection(id, projectId, type, content, status, updatedAt), PromptExport(id, projectId, promptText, variant, createdAt), Version(id, projectId, snapshotJson, createdAt), ShareLink(id, projectId, token, expiresAt, revokedAt), Comment(id, projectId, sectionId, userId, body, createdAt). Implementation requirements: Use server actions or API routes for generation requests, queue long-running LLM jobs, validate structured outputs with a schema, autosave edits, track analytics events, and enforce read-only permissions on shared links by default. Include responsive, accessible UI with keyboard navigation and high-contrast design. Build the app so the canonical PRD data drives both the document renderer and the exported coding prompt.
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