PRD for Claude Code: Structure, Examples, and Best Practices
This page shows you exactly how to structure a PRD for Claude Code, splitting specs into testable, file-scoped tasks with test scenarios. You’ll get a filled Claude Code PRD example, real-world comparisons to alternative AI coding tools, and step-by-step processes you can use for your next project.
What this is
A PRD for Claude Code is a product requirements document written to exploit Claude Code’s strengths: breaking down specs into discrete, testable tasks that specify file paths and public API changes. Unlike generic AI code tools, Claude Code requires granular instructions for each change, enabling repeatable builds and clear scope. A PRD designed for Claude Code typically lists feature tasks, file/folder-level changes, and test scenarios per task. This approach is particularly relevant for PMs used to tools like Replit, Cursor, or Codex, but takes more effort upfront for clear, reliable agent execution.
Compared to alternatives
| Option | Best for | Trade-off |
|---|---|---|
| Claude Code | Founders/PMs wanting detailed agent-driven development; granular testable tasks; rapid iteration. | Requires breaking features into many small tasks, with stricter spec discipline. |
| Codex by OpenAI | Quickly prototyping single-file scripts or code snippets. | Less reliable on multi-file, multi-step builds; less strict about file structure. |
| Replit Ghostwriter | Live coding and suggestions within an online IDE; real-time pairing. | Doesn’t enforce task or file structure; less suited for terminal-native agents. |
| Cursor | Codebase navigation and inline code generation in editor; pair-programming workflows. | Doesn’t support full agent workflows; relies on human context management. |
| Lovable | Spec-first, clear PRDs with user-journey mapping and API integration. | More focused on AI-generated product specs than shipping code directly. |
A real example
Product: QuickPoll CLI - A command-line survey tool that lets users create, launch, and tally polls via terminal. Built for instant feedback from distributed teams. Tech: Node.js, outputs JSON and CSV.
Goal: 3 core capabilities in 1 week; codebase under /quickpoll/ with clear CLI entrypoints.
Tasks:
-
Setup CLI Entrypoint (file: /quickpoll/cli.js; API: startCLI(), help())
- Implement CLI parser with yargs.
- Expose commands: create, vote, results.
- Test scenario: Running 'node cli.js --help' lists all options; 'node cli.js create' prompts for title.
-
Local Data Storage (file: /quickpoll/storage.js; API: savePoll(), loadPolls())
- Store polls and votes in local JSON. Handle concurrent writes.
- Test scenario: After creating a poll, ensure it’s persisted. Simulate race condition with two votes at once.
-
Poll Creation Module (file: /quickpoll/poll.js; API: createPoll(), endPoll())
- Accept title, options via prompt or CLI flags. Validate input (2-10 options).
- Test scenario: User attempts to submit only 1 poll option—error thrown. Creating 3 polls and then ending one marks it as closed.
-
Voting and Result Tally (file: /quickpoll/vote.js; API: vote(), tallyResults())
- Allow users to vote on non-closed polls. Prevent double voting by same user (track by username).
- Test scenario: User 'sam' votes twice in same poll; only first is counted. Tally after votes matches stored data.
-
Export Results Feature (file: /quickpoll/export.js; API: exportToCSV(), exportToJSON())
- Provide CLI flags to export poll results. Save to /quickpoll/exports/ directory.
- Test scenario: Export after 10 votes produces well-formed CSV and JSON. Directory created if missing.
Acceptance metrics:
- All features manually tested: create, vote, end poll, export.
- 90%+ unit test coverage (Jest).
- Ship within 7 days. Code review by different engineer before merge.
How to use this
- Break down features into atomic tasks: List out each feature as a discrete, testable task. Avoid broad tasks like 'build dashboard'—instead, specify UI, data fetch, and export as separate steps.
- Specify file paths and APIs per task: For every task, name the file(s) changed and the public API(s) exposed. Claude Code uses this to scope changes and avoid overwriting unrelated code.
- Write test scenarios for each task: Include clear, machine-testable scenarios: CLI input/outputs, error conditions, data validations. This helps Claude Code validate its outputs.
- Sequence dependencies explicitly: Mention pre-requisites for each task. Example: 'Implement poll.js before vote.js'. Claude Code will tackle tasks in this order.
- Summarize acceptance criteria and metrics: State explicit quality bars: time to ship, test coverage %, specific manual or user validation steps. Claude Code works best with clear done definitions.
- Review and refine for clarity: Before sending to Claude Code, reread your PRD. Ask: could a new engineer with no context build this step by step?
FAQ
How is a PRD for Claude Code different from a normal PRD?
A PRD for Claude Code is engineered for agent execution. Each feature is a testable, independent task, mapped to file changes and explicit API exposure—instead of broad requirements or user stories. You’ll also include concrete test scenarios and acceptance metrics, making it instantly usable for automated agent workflows.
What level of detail does Claude Code need in the PRD?
You need to specify file paths, API function names, and test cases for each task. Don’t just say 'add login'—spell out files like 'auth.js', routes, and scenarios like 'user enters wrong password'. This deters bugs and helps Claude Code ship 10x faster and more reliably than ‘write a login screen’ prompts.
Can I use templates or do I have to start from scratch?
You don’t have to reinvent the wheel. Grab PRD patterns and real examples from the PRD template hub or Example PRDs. Adapt them for your own toolset—Node.js, Next.js, Stripe integrations—and plug in your desired feature breakdown.
What file structure works best for Claude Code?
Claude Code excels with file/folder mapping: each task targets named paths. Organize by feature (e.g., /user/, /poll/) with dedicated test files. Keep CLI entrypoints, core logic, and exports in separate top-level files for clarity and easier agent iteration.