ChatPRD

You start with a sentence or a pile of notes, not a blank page. ChatPRD asks for the missing pieces, then writes goals, requirements, success metrics, and open questions in a consistent order. The draft is editable, so you can keep your own structure and tone.

A scorecard grades a draft against common PRD standards and shows where it falls short. Feedback is specific: a vague success metric, a missing edge case, a requirement with no owner. It works on documents you wrote yourself, which makes it useful as a review step rather than a writing tool.

A project keeps templates, reference docs, and past decisions in one place. Every new chat in that project can draw on that context, so you stop re-explaining your product on each prompt. This is the feature that separates a product management AI from a general chatbot.

The library covers PRDs, one-pagers, user stories, technical specs, and go-to-market briefs. Pro users can build custom templates and share them with a team. The built-in set is a decent starting point, and the custom option matters once your team has its own review format.

ChatPRD pulls context from Notion, Linear, GitHub, and Slack, and pushes finished docs back to those tools. On paid plans, an MCP connector lets Cursor, VS Code, Claude, ChatGPT, Codex, and Windsurf read the same authenticated documents. That means the AI coding tool sees the spec, not a summary of it.

Pro and Teams plans include a hosted REST API and MCP endpoint for listing, searching, reading, and generating documents. Keys are read-only unless you create them with write access. There is also a beta path that sends a spec into v0, Cursor, Lovable, or Replit as a starting point for a prototype.