Future Use Cases for Learning & Experimentation\n\n## Overview\n\nThese are real-world AI use cases you can implement to deepen your learning and expand the system.\n\n## Use Case Matrix\n\n| Use Case | Type | Complexity | ROI | Skills Needed | Time |\n|----------|------|-----------|-----|----------------|------|\n| Review Sentiment | Classification | ⭐ Low | ⭐⭐ Medium | llm-integration, testing | 1 day |\n| Dynamic Pricing | Prediction | ⭐⭐ Medium | ⭐⭐⭐ High | All | 3 days |\n| Review Summary | Extraction | ⭐⭐ Medium | ⭐⭐ Medium | All | 2 days |\n| Fraud Detection | Classification | ⭐⭐⭐ High | ⭐⭐⭐ High | All | 4 days |\n| Demand Forecasting | Prediction | ⭐⭐⭐ High | ⭐⭐⭐ High | All | 4 days |\n| Content Moderation | Classification | ⭐⭐ Medium | ⭐⭐⭐ High | llm-integration, testing | 2 days |\n| QA Chatbot | Generation | ⭐⭐⭐ High | ⭐⭐⭐ High | All | 5 days |\n| Auto-tagging | Classification | ⭐ Low | ⭐⭐ Medium | llm-integration, observability | 1 day |\n\n---\n\n## Tier 1: Easy (Start Here!)\n\n### 1️⃣ Review Sentiment Analysis\n\nProblem: Understand customer sentiment from reviews\n\nSolution: Classify reviews as positive/negative/neutral\n\nArchitecture:\n\nReview Input → AI Classification → Store Sentiment → Dashboard\n\n\nPrompt: prompts/add-use-case.md (Classification pattern)\n\nSkills Needed:\n- llm-integration (for Claude/OpenAI)\n- observability (track sentiment distribution)\n\nSuccess Metrics:\n- Accuracy: >85% match with human raters\n- Cost: <\(0.0001 per review\n- Latency: <500ms\n\n**Code Structure**:\njava\nReviewSentimentRequest: review text\nSentiment enum: POSITIVE, NEGATIVE, NEUTRAL\nReviewSentimentResponse: sentiment, confidence, reasoning\n\n\nLearning: Prompt engineering for classification\n\n---\n\n### 2️⃣ Product Auto-Tagging\n\nProblem: Products need consistent tags (category, type, use case)\n\nSolution: AI generates tags from description\n\nArchitecture:\n\nProduct Description → AI Tag Generation → Store Tags → Filter by tags\n\n\nPrompt: prompts/add-use-case.md (Generation pattern)\n\nSkills Needed:\n- llm-integration\n- testing-strategy\n\nSuccess Metrics:\n- Tag relevance: >80% human agreement\n- Coverage: 95%+ of products\n- Speed: <1 second per product\n\nLearning: Structured output from AI (tags must match predefined list)\n\n---\n\n## Tier 2: Medium (Next Level)\n\n### 3️⃣ Review Summary Generation\n\nProblem: Long product has 10K reviews - users won't read them\n\nSolution: AI generates concise summary (top pros/cons)\n\nArchitecture:\n\nReviews (1K+) → Batch Summary → Store Summary → Display on product page\n\n\nPrompt: prompts/add-use-case.md (Generation + batching)\n\n**Skills Needed**:\n- llm-integration\n- performance-optimization (batching)\n- observability\n\n**Success Metrics**:\n- Summary quality: >4/5 user rating\n- Cost: <\)0.001 per product\n- Accuracy: Key points match reviews\n\nLearning: Context compression, batching optimization\n\n---\n\n### 4️⃣ Dynamic Pricing Optimization\n\nProblem: Fixed prices miss revenue opportunities\n\nSolution: AI recommends prices based on demand, competition, margins\n\nArchitecture:\n\nProduct Data → Market Data → AI Analysis → Price Recommendation → Test A/B\n\n\nPrompt: prompts/add-use-case.md (Prediction pattern)\n\nSkills Needed:\n- llm-integration (for analysis)\n- performance-optimization (caching)\n- observability (track revenue impact)\n\nSuccess Metrics:\n- Revenue impact: >10%\n- Margin improvement: >5%\n- A/B test wins: >55%\n\nLearning: Business metrics, A/B testing, revenue models\n\n---\n\n### 5️⃣ Content Moderation\n\nProblem: User-generated content needs screening (spam, offensive, etc.)\n\nSolution: AI classifies content (allow/review/block)\n\nArchitecture:\n\nUser Content → AI Classification → Action (publish/review/block) → Log decision\n\n\nPrompt: prompts/add-use-case.md (Classification pattern)\n\nSkills Needed:\n- llm-integration\n- error-handling (false positives/negatives)\n- observability (moderation metrics)\n\nSuccess Metrics:\n- Precision: >98% (minimize false blocks)\n- Recall: >80% (catch actual spam)\n- User appeals: <5%\n\nLearning: False positive/negative trade-offs, safety\n\n---\n\n## Tier 3: Hard (Advanced)\n\n### 6️⃣ Fraud Detection\n\nProblem: Detect fraudulent orders before they cause damage\n\nSolution: ML + AI analyze order patterns\n\nArchitecture:\n\nOrder Data → ML Risk Score → AI Analysis → Block/Review Decision → Feedback Loop\n\n\nPrompt: prompts/add-use-case.md + design decision matrix\n\nSkills Needed:\n- All: llm-integration, performance-optimization, observability, error-handling, testing\n\nSuccess Metrics:\n- False positive rate: <1%\n- Catch rate: >90%\n- Cost per fraud prevented: <$5\n \nLearning: Multi-model systems, feedback loops, risk management\n\n---\n\n### 7️⃣ Demand Forecasting\n\nProblem: Stock too much = waste, too little = lost sales\n\nSolution: AI + historical data predict demand\n\nArchitecture:\n\nHistorical Data → Time Series Model → AI Context → Forecast → Inventory Decision\n\n\nPrompt: prompts/add-use-case.md (Prediction + time series)\n\nSkills Needed:\n- All skills + data science\n\nSuccess Metrics:\n- MAPE: <15% (mean absolute percent error)\n- Inventory cost: -20%\n- Stockouts: -50%\n\nLearning: Time series, ensemble models, business impact\n\n---\n\n### 8️⃣ QA Chatbot\n\nProblem: Support team spends time on FAQs\n\nSolution: AI chatbot answers common questions\n\nArchitecture:\n\nUser Question → Intent Detection → Context Retrieval → AI Generation → Human Review\n\n\nPrompt: prompts/add-use-case.md (Generation + conversation)\n\nSkills Needed:\n- All skills + conversation management\n\nSuccess Metrics:\n- Resolution without escalation: >70%\n- User satisfaction: >4/5\n- Cost savings: 40% of support questions\n\nLearning: Conversation state, context, escalation logic\n\n---\n\n## Implementation Path\n\n### Week 1: Learn\n\nDay 1-2: Read all documentation\nDay 3-4: Study one Tier 1 use case in depth\nDay 5: Practice explaining architecture\n\n\n### Week 2: Implement Tier 1\n\nDay 1: Set up Review Sentiment (2-3 hours)\nDay 2: Write tests and documentation\nDay 3: Add monitoring and optimize\n\n\n### Week 3: Tier 1 Reflection + Tier 2\n\nDay 1-2: Deploy and measure Review Sentiment impact\nDay 3-5: Implement Review Summary (batching focus)\n\n\n### Week 4+: Tier 2/3 + Production\n\nContinue with progressively harder use cases\nOptimize based on real metrics\nShare learnings with team\n\n\n---\n\n## Decision Framework\n\nChoose next use case based on:\n\nIf you want to learn about...\n- Prompt engineering → Review Sentiment or Auto-tagging\n- Batching & performance → Review Summary\n- Classification → Fraud Detection or Content Moderation\n- Prediction → Dynamic Pricing or Demand Forecasting\n- Conversation → QA Chatbot\n- Full stack → Dynamic Pricing or Fraud Detection\n\nIf you want to maximize ROI\n- Review Sentiment (low effort, medium ROI)\n- Dynamic Pricing (medium effort, high ROI)\n- Fraud Detection (high effort, high ROI)\n\nIf you want to impress in interviews\n- Start with Review Sentiment (mastery)\n- Add Dynamic Pricing (business sense)\n- Top it with Fraud Detection (full system design)\n\n---\n\n## Resource Links\n\nFor each use case:\n1. Skills: Read .github/skills/\n2. Prompt: Use relevant prompt from /prompts/\n3. Agents: Use llm-integration or test-generation\n4. Documentation: Create in /docs/use-cases/\n\n---\n\n## Success Criteria\n\nWhen your use case is production-ready:\n\n✅ Code\n- [ ] Compiles without warnings\n- [ ] Tests >80% coverage\n- [ ] Follows existing patterns\n- [ ] Error handling complete\n- [ ] Monitoring hooks added\n\n✅ Documentation\n- [ ] Architecture diagram\n- [ ] Cost analysis\n- [ ] Decision matrix\n- [ ] Monitoring setup\n- [ ] Interview Q&A\n\n✅ Operations\n- [ ] Metrics baseline\n- [ ] Alerts configured\n- [ ] Runbook created\n- [ ] Team trained\n- [ ] Fallback tested\n\n---\n\nGet started with Tier 1 (Review Sentiment) today!\nIt takes 1 day and teaches you 80% of what you need for Tier 2.\n"