Plan

From a 4-page PRD to 15 stories, 60 acceptance criteria, and a sprint draft in 90 seconds.

AI that turns a PRD into a sprint-ready backlog — with acceptance criteria, tests, and dependencies.

Translating a PRD into actionable stories takes most PMs a half-day per epic. Stride generates the epic, story breakdown, acceptance criteria, test cases, and dependency graph in under two minutes — leaving the PM to edit instead of author.

Outcome

PRD-to-sprint-ready time drops from ~half a day to ~30 minutes (editing time)

Stride telemetry, Q1 2026 (n=200 PRDs)

The problem

Product managers spend a third of every week translating PRDs into stories that engineering can build. The work is mechanical (split this paragraph into 4 stories, write 5 AC per story, identify dependencies) but it doesn't scale — every new PRD eats a half-day. Worse, the AC quality varies with the PM's energy and the story dependencies often get missed until mid-sprint when an engineer hits the blocker.

How Stride solves it

Drop a 4-page PRD into Stride. In 90 seconds you have a draft epic, 10-20 stories with rationale, 5-8 acceptance criteria per story (Gherkin format), test case skeletons, and an inferred dependency graph. PM edits the output; engineers can start the sprint refinement same-day.

PRD → epic + stories breakdown with rationale per split
Acceptance criteria in Gherkin format, 5-8 per story
Surface-level coverage check: every PRD section has at least one story
Dependency inference from cross-story references
Story sizing estimates with confidence intervals
Out-of-scope list per story (clarifies what each story does NOT do)

Best for

PM-led teams who write traditional PRDs (4-10 pages, structured) and need to translate them into sprint-ready stories quickly.

Not for

Teams that skip PRDs entirely and work from one-line story descriptions. The AI works best when the input has enough structure to decompose — a 100-word product spec produces a thin output.

Frequently asked

Does the AI understand my domain?

It learns from your existing stories. The first PRD generates stories using mostly generic patterns; by the third PRD, the model is matching your domain vocabulary, common API patterns, and your team's preferred story-splitting style. You can also provide examples of your best stories as anchors.

What PRD format works best?

Any structured doc with sections. Headings help (the AI splits epics along major sections); user-story-format ("As a user I want X so that Y") snippets help; explicit acceptance criteria in the PRD help. Pure free-form prose works but produces lower-fidelity output.

How does this interact with existing stories?

When you import a PRD that overlaps with existing work, the AI flags potential duplicates and suggests linking to the existing story instead of creating a new one. Cross-references emerge from the project graph, not a string-match search.

Can it generate test cases too?

Yes — each story gets test case skeletons (positive path, error path, edge cases) tied to its AC. These are starting points for QA, not final tests. See /use-cases/ai-test-generation for the test-generation workflow.

See ai prd generation in Stride

14 days of Stride Pro, no credit card. The sample project includes every module so you can explore end-to-end in five minutes.

Start free