A PRD generator that actually verifies its output
15 thinking strategies from research papers. 6 verification algorithms. 9 deliverables per generation. It is not perfect, but it catches things a single LLM pass never will. Free and open source.
> /plugin install ai-prd-generator
Requires macOS 14+ with Apple Silicon (M1/M2/M3/M4) · Works with any stack · See FAQ
How to Generate a PRD in 2 Minutes
Four steps from clone to verified requirements. No configuration needed.
Install
Two commands in Claude Code. The marketplace handles dependencies automatically.
> /plugin install ai-prd-generator
Open Claude Code
Launch Claude Code in your terminal. Navigate to your project directory if you want codebase-aware PRD generation.
$ claude
Describe Your Feature
Use natural language or the slash command. The generator auto-detects the PRD type, selects an optimal thinking strategy, and begins structured clarification questions. Answer them and tell it when to proceed.
Review Your Deliverables
The generator produces 9 files in your project directory: overview, requirements, technical architecture, user stories, roadmap, test cases, JIRA tickets, acceptance criteria, and a verification report. Review, iterate, or feed them directly into the full autonomous pipeline.
prd-overview.md
prd-requirements.md
prd-technical.md
prd-user-stories.md
prd-roadmap.md
prd-tests.md
prd-jira.md
prd-acceptance.md
prd-verification.md
Describe what you need. Answer clarification questions. Get nine files. The process is the same whether it is a greenfield feature or a complex migration — but the results will vary. Simple features come out clean. Complex ones need more human review.
What this PRD generator does differently
Most PRD tools generate a document and call it done. This one verifies it.
Most AI PRD generators take a prompt, run it through a single LLM call, and hand back an unverified document. There is no way to know whether requirements are internally consistent, whether acceptance criteria map to stated goals, or whether the technical approach contradicts known constraints. AI Architect treats PRD generation as a multi-phase process: generate, decompose into atomic claims, then run 6 independent verification algorithms against those claims. The result is a document with a claim-by-claim audit trail.
It selects from 15 research-based thinking strategies rather than relying on a single prompt template. A simple bug report uses Zero-Shot. A complex system migration uses Tree of Thoughts with multi-perspective analysis. And instead of one document, every generation produces nine deliverables: product overview, requirements, technical architecture, user stories, roadmap, test cases, JIRA tickets with story points, acceptance criteria, and a verification report.
Is it perfect? No. The verification catches structural issues and contradictions, not domain expertise gaps. The strategies help with reasoning, not with knowing your business. It is a tool that does the mechanical work well so you can focus on the judgment calls.
15 Research-Based Strategies to Generate Better PRDs
Drawn from MIT, Stanford, Anthropic, OpenAI, and DeepSeek research. The generator selects the optimal strategy based on your request complexity.
Zero-Shot
Direct generation for straightforward, well-defined requests.
Chain of Thought
Step-by-step reasoning through requirements and dependencies.
Few-Shot
Pattern matching against exemplar PRDs for consistent structure.
Tree of Thoughts
Branching exploration of multiple solution paths before committing.
ReAct
Interleaved reasoning and action for requirements needing real-time data.
Reflexion
Self-evaluation and iterative refinement of generated sections.
Meta-Prompting
Dynamic prompt construction tuned to the specific problem domain.
Self-Consistency
Multiple generation paths with consensus voting on final output.
MCTS
Monte Carlo Tree Search for exploring complex requirement spaces.
R1-Style Reasoning
Deep chain reasoning inspired by DeepSeek R1 methodology.
Analogical Reasoning
Draws parallels from similar solved problems in indexed research.
Constraint Satisfaction
Ensures all technical and business constraints are simultaneously met.
Decomposition
Breaks complex features into independently implementable sub-problems.
Multi-Perspective
Analyzes requirements from engineering, product, UX, and security viewpoints.
Hybrid Fusion
Combines multiple strategies when no single approach is sufficient.
The generator selects the strategy automatically based on request type, complexity, and ambiguity level. You do not need to choose — it adapts its reasoning approach rather than forcing every request through the same template.
8 PRD Types — Feature, Bug, Incident, Proposal, MVP, POC, Release, CI/CD
Each type adapts clarification depth, section structure, strategy selection, and verification rigor.
| Type | Focus | Sections |
|---|---|---|
| Feature | Full technical implementation with architecture, API specs, and data models | 11 |
| Bug | Root cause analysis with reproduction steps and regression prevention | 6 |
| Incident | Deep forensic investigation with timeline, impact radius, and remediation | 8 |
| Proposal | Business case with ROI analysis, competitive landscape, and risk assessment | 7 |
| MVP | Core value identification with fastest path to validation and learning metrics | 8 |
| POC | Feasibility validation with success criteria and evaluation framework | 5 |
| Release | Production readiness with rollout plan, monitoring, and rollback procedures | 10 |
| CI/CD | Pipeline automation with stage definitions, quality gates, and deployment targets | 9 |
Each type structures the output differently. A Feature PRD includes 11 sections covering user stories through database schemas. A Bug PRD focuses on 6 sections around root cause analysis and prevention. The type also drives strategy selection — a simple bug report uses Zero-Shot, a complex release with cross-service dependencies engages Tree of Thoughts or Multi-Perspective analysis.
6 Verification Algorithms — Every Claim Checked
Most PRD generators do not verify their output. These 6 algorithms run independently against every statement in the document.
Multi-Judge Consensus
Multiple independent LLM evaluators assess each section of the PRD. A claim must reach consensus across judges to pass. Disagreements are flagged with explanations, ensuring no single model's bias determines the outcome of verification.
Zero-LLM Graph Analysis
Builds a directed graph of all requirements, dependencies, and acceptance criteria without using any LLM. Detects circular dependencies, orphaned requirements, and structural inconsistencies through pure algorithmic analysis. This is deterministic and repeatable.
Multi-Agent Debate
Specialized agents argue for and against each claim from different perspectives: engineering feasibility, business value, security implications, and user impact. Claims that survive debate are marked verified. Those that do not are revised or flagged.
Atomic Claim Decomposition
Every paragraph in the PRD is decomposed into atomic, individually verifiable claims. Each claim is checked against the stated context, constraints, and goals. This prevents vague or compound statements from hiding unverified assumptions.
Adaptive Early Stopping
Monitors verification cost in real time and stops additional checks when confidence reaches a threshold. Simple, clear claims resolve quickly. Ambiguous or complex claims receive more scrutiny. This keeps LLM cost under control without sacrificing quality.
Semantic Verification
Cross-references the generated PRD against your actual codebase (if provided), indexed research papers, and stated constraints. Catches cases where the PRD specifies behavior that contradicts existing implementation or violates documented architectural decisions.
Together, these algorithms turn the generator from a drafting tool into something closer to a verified specification engine. The output includes a full verification report showing exactly which claims passed, which were flagged, and what evidence was used. It does not catch everything — domain-specific errors still need human review — but it catches structural and logical issues that a single LLM pass will miss every time.
9 Deliverables Per Generation
One input, nine structured outputs. No manual translation step between spec and engineering handoff.
Product Overview
Problem statement, goals, success metrics, and stakeholder alignment. The executive summary that frames the entire specification.
Requirements
Numbered functional and non-functional requirements with priority levels. Each requirement is testable and traceable.
Technical Architecture
System design, API specifications, data models, sequence diagrams, and infrastructure requirements grounded in your actual codebase.
User Stories
Persona-based stories with acceptance criteria. Covers happy paths, edge cases, and cross-cutting concerns.
Roadmap
Phased delivery plan with milestones, dependencies, and completion criteria. Sprint-ready breakdown.
Test Cases
Structured test suite with preconditions, steps, expected results, and traceability matrix mapping each test to its source requirement.
JIRA Tickets
Ready-to-import tickets with titles, descriptions, story points, priority, and labels. Each ticket traces back to a specific requirement.
Acceptance Criteria
Given/When/Then format acceptance criteria for every requirement. Directly usable by QA for validation.
Verification Report
Claim-by-claim audit trail showing which verification algorithms ran, what they found, and the final confidence score.
Every file is derived with full traceability — requirements map to user stories, test cases map to acceptance criteria, JIRA tickets map to requirements. The verification report shows whether the document is internally consistent. Nine files, zero translation step between writing requirements and acting on them.
AI PRD Generator Comparison
How AI Architect stacks up against other tools for generating product requirements documents.
| Feature | AI Architect | ChatPRD | Figma AI | Miro AI | ChatGPT |
|---|---|---|---|---|---|
| PRD Types | 8 specialized | 1 generic | 1 generic | 1 generic | 1 generic |
| Thinking Strategies | 15 research-based | 1 (single prompt) | 1 (single prompt) | 1 (single prompt) | 1 (single prompt) |
| Verification | 6 algorithms | None | None | None | None |
| Deliverables | 9 files | 1 document | 1 document | 1 document | 1 document |
| JIRA Tickets | ✓ | ✗ | ✗ | ✗ | ✗ |
| Test Cases | ✓ | ✗ | ✗ | ✗ | ✗ |
| Codebase Analysis | ✓ | ✗ | ✗ | ✗ | ✗ |
| Clarification Rounds | Unlimited | None | None | None | Manual |
| Open Source | ✓ | ✗ | ✗ | ✗ | ✗ |
| Free & Open Source | Fully free | Limited | Included | Included | Limited |
| Runs Locally | ✓ | ✗ | ✗ | ✗ | ✗ |
Get Started
Free and open source. All features included. Clone, install, generate.
> /plugin install ai-prd-generator
Frequently Asked Questions About the PRD Generator
Common questions about setup, capabilities, and how the AI PRD generator works.
A PRD generator is a tool that automates the creation of Product Requirements Documents. Instead of spending hours manually writing specifications, a PRD generator takes your feature description, asks targeted clarification questions, and produces a structured document with technical requirements, acceptance criteria, and implementation guidance.
AI Architect's PRD generator goes beyond basic document creation. It uses 15 research-based thinking strategies to reason through your requirements, asks multi-round clarification questions to resolve ambiguity, analyzes your actual codebase to ground the PRD in real architecture, and verifies every claim with 6 independent algorithms. The output is not a rough draft — it is a verified specification with JIRA tickets, test cases, and an audit trail.
Yes. The PRD generator is completely free and open source. All features are included:
- All 15 thinking strategies
- All 8 PRD types
- Unlimited clarification rounds
- 6 verification algorithms
- 9 deliverables (overview, requirements, architecture, stories, roadmap, tests, tickets, acceptance, verification)
- Codebase analysis
No license key required. Install from the Claude Code marketplace and start generating.
ChatPRD generates a single document from a single prompt. AI Architect differs in three fundamental ways:
- Verification: AI Architect runs 6 independent algorithms against every claim. ChatPRD provides no verification.
- Strategies: AI Architect selects from 15 research-based thinking strategies. ChatPRD uses a single prompt template.
- Output: AI Architect produces 9 deliverables (overview, requirements, architecture, stories, roadmap, tests, tickets, acceptance, verification). ChatPRD produces 1 document.
AI Architect is also open source and runs locally as a Claude Code plugin, meaning your data never leaves your machine.
Yes. AI Architect is stack-agnostic and works with iOS (Swift, SwiftUI), Android (Kotlin, Jetpack Compose), React Native, Flutter, and any other mobile framework. The codebase analysis feature reads your project structure and adapts the PRD to your actual architecture patterns, dependencies, and conventions.
If you point the generator at an existing iOS project, for example, it will reference your Swift protocols, data models, and view hierarchy in the generated requirements. The same applies to Android, cross-platform, or backend projects.
AI Architect works with Python, TypeScript, JavaScript, Go, Rust, Java, Kotlin, Swift, C#, Ruby, PHP, and more. The PRD generator is language-agnostic — it adapts to whatever stack your project uses.
The codebase analysis feature automatically detects your technology stack and references actual code patterns, directory structures, and architecture decisions in the generated PRD. You do not need to configure language support; it is detected from your project files.
Yes. AI Architect is a plugin (skill) for Claude Code (Anthropic's CLI for Claude) and Cowork (Claude Desktop). You need an active subscription from Anthropic for either product.
The PRD generator itself is free and open source. It runs as a local skill inside Claude. No separate API keys, no cloud services, no data sent to third parties. Your code and requirements stay on your machine.
Asking ChatGPT (or any generic LLM) to write a PRD gives you a single-pass document with no verification, no strategy selection, and no codebase awareness. The output may look well-structured, but there is no mechanism to check whether the generated requirements are internally consistent or technically feasible.
AI Architect:
- Selects from 15 strategies based on your request complexity
- Asks structured clarification questions before generating
- Analyzes your actual codebase for architecture-grounded requirements
- Verifies every claim with 6 independent algorithms
- Produces 9 deliverables instead of 1 rough draft