Create AI Application

# Create an AI Application Plan

You are a senior AI engineer and product architect. Help me design and build a practical AI-powered application.

## Application Details
- **App Name / Working Title:** [APP_NAME]
- **Core Problem It Solves:** [PROBLEM]
- **Target Users:** [TARGET_USERS]
- **Primary AI Functionality:** [AI_FUNCTIONALITY] (e.g., natural language processing, image recognition, recommendation engine, AI chatbot, content generation, predictive analytics)
- **Platform:** [PLATFORM] (e.g., web app, iOS/Android mobile app, browser extension, API service, desktop app)
- **Technical Skill Level:** [SKILL_LEVEL] (beginner / intermediate / advanced)
- **Budget / Resources:** [BUDGET]

## Architecture Plan

### 1. Product Requirements Document (PRD) Summary
- Problem statement and target user personas
- Core features (MVP — must-have) vs. nice-to-have features
- Success metrics: how will you know the app works?

### 2. AI Model Selection
- Recommended AI model(s): OpenAI API, Anthropic Claude API, Google Gemini, Hugging Face open-source, or custom-trained model
- Rationale for each choice based on [AI_FUNCTIONALITY]
- Estimated API costs at 100, 1,000, and 10,000 monthly users
- Fine-tuning vs. prompt engineering vs. RAG: which approach fits this app

### 3. Tech Stack Recommendation
| Layer | Technology | Why |
|---|---|---|
| Frontend | [RECOMMENDED] | [REASON] |
| Backend | [RECOMMENDED] | [REASON] |
| AI/ML Layer | [RECOMMENDED] | [REASON] |
| Database | [RECOMMENDED] | [REASON] |
| Auth | [RECOMMENDED] | [REASON] |
| Hosting | [RECOMMENDED] | [REASON] |

### 4. Data Architecture
- Data inputs the AI will process
- Storage and privacy considerations
- How to handle user data responsibly (GDPR, CCPA)

### 5. MVP Build Roadmap
- Week 1-2: Environment setup, API integration, core AI feature
- Week 3-4: UI/UX foundation, user auth, database
- Week 5-6: Testing, iteration, beta user feedback
- Week 7-8: Launch prep, monitoring, documentation

### 6. Prompt Engineering Guide
- System prompt template for [AI_FUNCTIONALITY]
- Input validation and output parsing strategy
- Error handling for AI failures or unexpected outputs

### 7. Launch & Monetization
- Beta launch checklist
- Monetization models: freemium, subscription, usage-based, one-time
- Distribution: Product Hunt, app store optimization, developer communities

Deliver a complete application blueprint ready to share with a developer or co-founder.