PRD-driven development uses a product requirements document to guide what gets built, how work is split, and how implementation is checked.
Definition
PRD-driven development is an implementation workflow where a product requirements document guides what gets built, what is out of scope, and how the result is validated. The PRD becomes the source of truth for product intent, not just a planning artifact.
For AI coding agents, the value is especially direct. A PRD turns an open-ended request into a bounded implementation target with acceptance criteria, constraints, and review expectations.
Why it matters
AI coding agents can move quickly, but speed without structure creates review burden. A PRD helps keep the loop aligned by defining the user problem, required behavior, non-goals, edge cases, and validation commands before implementation begins.
For one person companies, this reduces the cost of context switching. The founder can write or approve requirements once, then use those requirements to guide planning, coding, validation, and release review.
Example
Instead of telling an agent “improve onboarding,” a PRD-driven workflow defines the onboarding problem, target user, screens or commands involved, required behavior, and what must pass before the work is done.
Hal applies this idea to coding loops by converting requirements into stateful implementation iterations. The loop can initialize project workflow files, plan or capture a PRD, convert requirements into runtime state, validate the workflow, and run implementation iterations.
Related concepts
- Product requirements document defines the source document.
- AI coding agent is the implementation engine in the loop.
- Run a PRD-driven coding loop with Hal shows the command-forward workflow.
Sources
- Atlassian: Product requirements document
- ProductPlan: Product requirements document
- Hal source repository
- Last checked: 2026-05-09
FAQ
What is PRD-driven development?
PRD-driven development uses a product requirements document as the source of truth for implementation, validation, and review.
Why use a PRD with AI coding agents?
A PRD gives the agent clear scope, constraints, acceptance criteria, and non-goals, which makes output easier to review.
What should a PRD include for an agent loop?
A useful PRD includes the problem, target user, required behavior, constraints, acceptance criteria, and validation steps.
Related pages
Next step
Run autonomous PRD-driven coding loops with AI agents.
View Hal on GitHub