| name | prd |
| description | Generate an ML-centric PRD for ML-Ralph. Use when planning an ML project, experiment plan, or when asked to create an ML PRD. Triggers on: create a prd, write prd for, plan this ML feature, requirements for, spec out. |
ML PRD Generator
Create ML-centric PRDs that are evidence-driven, stack-agnostic, and suitable for ML-Ralph.
The Job
- Receive a project description
- Ask 3-5 essential clarifying questions (one at a time)
- Generate a structured ML PRD
- Save to
tasks/prd-[feature-name].md
Important: Do NOT start implementing. Just create the PRD.
Step 1: Clarifying Questions (One at a Time)
Focus on:
- Objective/Metric: What is success? What metric matters?
- Data Context: What data exists? Any leakage risks? Any constraints?
- Evaluation: What validation scheme is appropriate?
- Scope: What should NOT be done?
Example format:
1) What is the primary objective?
A. Classification
B. Regression
C. Ranking
D. Other: [specify]
Step 2: PRD Structure
Generate the PRD with these sections:
1. Introduction/Overview
Brief description of the ML task and why it matters.
2. Goals
Specific, measurable objectives (bullet list).
3. Assumptions
Explicit assumptions (data availability, metric definitions, constraints).
4. Evaluation Plan
- Metric definition
- Split strategy (random/stratified/group/time)
- Leakage rules
5. User Stories (ML-centric)
Each story must include:
- Title
- Description
- Type: discovery | experiment | evaluation | implementation | ops
- Hypothesis (optional but preferred)
- Evidence Required (what must be logged, including W&B run URL/ID for experiment/evaluation stories)
- Acceptance Criteria (verifiable)
Format:
### US-001: [Title]
**Description:** As a [role], I want [outcome] so that [benefit].
**Type:** discovery | experiment | evaluation | implementation | ops
**Hypothesis:** If ..., then ... because ...
**Evidence Required:** [What must appear in progress.txt or artifacts; include W&B run URL/ID for experiment/evaluation stories]
**Acceptance Criteria:**
- [ ] Specific, verifiable criterion
- [ ] Another criterion
- [ ] Ruff check passes
- [ ] Ruff format passes
- [ ] Mypy passes
- [ ] Pytest passes (if tests exist)
- [ ] Evidence logged in progress.txt
Important:
- Stories must be small enough for one iteration.
- Acceptance criteria must be verifiable.
- Include evidence logging for every story.
6. Functional Requirements
Numbered list of required behaviors or components.
7. Non-Goals (Out of Scope)
Explicitly list what will not be done.
8. Risks / Uncertainties
Known unknowns and how they’ll be resolved.
9. Success Metrics
Define “done” in measurable terms.
10. Open Questions
Remaining questions that might alter the plan.
ML-Ralph Dynamic Backlog Guidance
PRDs are living documents. ML-Ralph may refine prd.json each iteration based on evidence:
- add/split/reorder/supersede stories
- never delete stories
- log changes in
progress.txt
Output
- Format: Markdown (
.md)
- Location:
tasks/
- Filename:
prd-[feature-name].md (kebab-case)
