The Problem

As a product manager, writing feature requirements, user stories, and acceptance criteria takes forever. Instead of thinking about product strategy, you're stuck writing documentation. So I built a web app that uses AI to do this automatically.

What It Does

You describe a feature in plain English, and the app gives you:

Feature Requirements (functional and technical specs)
User Stories (proper format with user types and benefits)
Acceptance Criteria (Given-When-Then format)

Tech Stack

Frontend

Next.js 15 with App Router (server-side rendering, clean integration)
React 18 (modern hooks, server components)
Tailwind CSS (quick styling)
shadcn/ui (quality components)
Lucide React (clean icons)

AI Integration

AI SDK by Vercel (works with multiple AI providers)
Groq (fast inference, generous free tier)

Development Tools

TypeScript (catch errors early)
Zod (validate AI responses)
Sonner (user notifications)

Deployment

Vercel (automatic deployments)
GitHub (version control)

Building Process

Phase 1: Setup

npx create-next-app@latest pm-requirements-generator --typescript --tailwind --app
cd pm-requirements-generator

Key Decision: Started with Next.js 15's App Router from day one. Saved me from refactoring later.

Phase 2: UI Components

npx shadcn@latest init
npx shadcn@latest add card button input textarea tabs badge

Used shadcn/ui instead of building from scratch. Saved weeks of work.

Phase 3: The AI Challenge

This part gave me serious headache. Tried three different AI providers:

Attempt 1: xAI (Grok)

const result = await generateObject({
  model: xai("grok-beta"), // This model didn't exist
  schema: RequirementsSchema,
  prompt,
})

Problem: Wrong model name, access issues.

Attempt 2: OpenAI

const result = await generateObject({
  model: openai("gpt-4o"),
  schema: RequirementsSchema,
  prompt,
})

Problem: Quota exceeded. No credits, no service.

Attempt 3: Groq (Finally worked)

const result = await generateObject({
  model: groq("llama-3.3-70b-versatile"),
  schema: RequirementsSchema,
  prompt,
})

Why Groq worked: Free tier, fast response, reliable API.

Phase 4: Structured Output

Used Zod schemas to ensure AI gives consistent responses:

const RequirementsSchema = z.object({
  requirements: z.array(z.string()).describe("Detailed functional and technical requirements"),
  userStories: z.array(z.string()).describe("User stories in format: As a [user], I want [goal] so that [benefit]"),
  acceptanceCriteria: z.array(z.string()).describe("Testable acceptance criteria using Given-When-Then format"),
})

Pro tip: Detailed schema descriptions = better AI output.

Phase 5: User Experience

Added essential features:

Copy-to-clipboard for each section
Markdown export
Loading states
Error handling
Toast notifications

Phase 6: Mobile Responsive

Mobile-first approach. Used CSS Grid:

<div className="grid grid-cols-1 sm:grid-cols-3 gap-3 sm:gap-4 md:gap-6">
  {/* Three feature cards */}
</div>

Phase 7: Deployment

git init
git add .
git commit -m "Initial commit: PM Requirements Generator"
git remote add origin https://github.com/username/pm-requirements-generator.git
git push -u origin main

Connected to Vercel, set up environment variables (GROQ_API_KEY), done.

Technical Implementation

Server Actions

Used Next.js Server Actions for secure AI processing:

"use server"
export async function generateRequirements(input: GenerateRequirementsInput) {
  try {
    const result = await generateObject({
      model: groq("llama-3.3-70b-versatile"),
      schema: RequirementsSchema,
      prompt: constructPrompt(input),
    })
    return { success: true, data: result.object }
  } catch (error) {
    return { success: false, error: "Failed to generate requirements" }
  }
}

Benefits: API keys stay secure, better error handling, faster processing.

Prompt Engineering

Crafted specific prompts for consistent results:

const prompt = `
You are an expert product manager. Generate comprehensive feature requirements, user stories, and acceptance criteria for the following feature:

Feature Description: ${featureDescription}
${projectContext ? `Project Context: ${projectContext}` : ""}
${targetAudience ? `Target Audience: ${targetAudience}` : ""}

Please generate:
1. REQUIREMENTS: 5-8 detailed functional and technical requirements...
2. USER STORIES: 4-6 user stories following the format "As a [user type], I want [goal] so that [benefit]"...
3. ACCEPTANCE CRITERIA: 6-10 specific, testable acceptance criteria...
`

Lesson: Specific, structured prompts work better than generic requests.

Problems I Faced

1. Model Deprecation

Issue: Groq model llama-3.1-70b-versatile was discontinued mid-development.

Solution: Updated to llama-3.3-70b-versatile and added error handling.

Lesson: Always have backup plans for external dependencies.

2. Mobile Layout Issues

Issue: Three-card layout didn't align properly on mobile.

Solution: CSS Grid with responsive breakpoints.

Lesson: Test on mobile devices throughout development.

Key Lessons

AI Provider Reliability Matters: Don't depend on one provider. Build abstractions that allow easy switching.
User Experience First: Loading states, error handling, and feedback are not optional.
Mobile-First Design: Most users will access your tool on mobile.
Validate AI Outputs: Use Zod schemas for runtime validation.
Environment Setup: Deployment configuration is often underestimated.

Performance Optimizations

Server-Side Processing: Moved AI calls to server actions for security and performance.
Loading States: Proper loading indicators improve user experience.
State Management: Used React's built-in state management to avoid complexity.

Future Plans

1. User Authentication

Add Supabase auth, save user requirements, enable versioning.

2. Template Library

Pre-built templates for common features (authentication, payments, search).

3. Integrations

Jira: Direct export to tickets
Confluence: Auto documentation
Slack: Share with team
GitHub: Create issues

4. Advanced AI Features

Effort estimation
Multi-model support
Real-time collaboration

Recommendations

If you want to build something similar:

Start Simple: Core functionality first, features later.
Iterate Quickly: Deploy early, get feedback.
Plan for Failure: Implement error handling from day one.
Mobile-First: Design for mobile from the beginning.
Monitor Usage: Track AI costs and performance.

Conclusion

Building this tool taught me that AI can solve real productivity problems when implemented properly. The key is choosing reliable technology, focusing on user experience, and planning for iteration.

The Next.js + AI SDK + Groq combination worked well for this use case. The AI SDK made it easy to switch between providers, which saved me when other services failed.

This project proves that with the right approach, you can build professional AI-powered tools that actually improve workflows. No unnecessary complexity, just practical solutions to real problems.

Bottom line: If you're solving a real problem with reliable technology and good user experience, you'll build something people actually use.

Building an AI-Powered Product Management Tool: A Complete Development Journey

Table of contents