AI

📚 Table of Contents The AI Feature That Went Wrong (And Why It’s Not the Model’s Fault) What AI Actually Is (And What It Isn’t) LLMs Demystified: The PM’s Mental Model RAG, Fine-Tuning, and When to Use What The AI Feature Development Process Specifying AI Features: The New Requirements AI Costs: Understanding the Token Economy Testing and Quality for AI Features Common AI Product Mistakes Building Your AI Product Muscles Questions to Ask Your AI/Engineering Team The Bottom Line The AI Feature That Went Wrong (And Why It’s Not the Model’s Fault) In 2024, I watched a company spend $300,000 building an AI feature that failed spectacularly. ...

📚 Table of Contents The AI Product Revolution Is Here What Makes AI Products Different Understanding LLMs: A PM’s Practical Guide RAG Explained: Making LLMs Smarter The AI Product Development Lifecycle Metrics for AI Products: Beyond Traditional KPIs Working with ML Engineers: Communication That Works AI Ethics and Responsible AI for PMs Tools and Platforms for AI Product Development Getting Started in AI Product Management The AI Product Revolution Is Here In 2023, I watched a product team spend six months building an AI feature. They hired expensive ML engineers, trained custom models, and invested heavily in infrastructure. When they launched, users… didn’t care. The feature worked technically but solved a problem nobody had. ...

Table of Contents The Silent Killer in Your AI Stack What the Hell is TOON Anyway? Why This Actually Matters The Four Fighters: TOON vs JSON vs CSV vs YAML Format Showdown: When to Use What Real Numbers: The TOON Savings Calculator Use Case Battle Royale The Visual Proof How to Actually Implement TOON The Honest Trade-Offs Quick Reference Cheat Sheet Your Next Steps The Silent Killer in Your AI Stack You know that feeling when your LLM API bill arrives and you think, “Wait, I’m paying HOW much just to send data back and forth?” ...

Table of Contents The Moment Everything Changed The Problem Nobody’s Talking About Normal Software: The Old World The Architecture Key Characteristics Real Example: E-commerce Checkout AI Software: The New World The Architecture How Token Generation Actually Works Key Characteristics Real Example: AI Email Assistant The Side-by-Side Comparison Let’s Dig Into Each Row What This Means for Product Teams 1. Feature Specification Must Change 2. Timeline Estimation Gets Fuzzy 3. QA Strategy Must Evolve 4. User Expectations Require Management 5. Error Handling Is Completely Different The Hybrid Architecture: How to Actually Build Products The Framework: When to Use What Example Hybrid Architecture Real Product Example: Notion AI The New Developer Skills (And What PMs Need to Know) 1. Prompt Engineering 2. Probabilistic Thinking 3. Model Evaluation 4. Hybrid Architecture Design Practical Frameworks for PMs Framework 1: The AI Feature Spec Template Framework 2: The AI Testing Checklist Framework 3: The Stakeholder Communication Template Common Mistakes (And How to Avoid Them) Mistake 1: Spec’ing AI Features Like Normal Features Mistake 2: Expecting Exact Timelines Mistake 3: Launching Without Feedback Mechanisms Mistake 4: Treating Hallucinations Like Bugs Mistake 5: Not Budgeting for Compute Costs Mistake 6: Assuming QA Knows How to Test AI The Future: What’s Coming Next 1. Multi-Modal Models 2. Agents (AI That Takes Actions) 3. Reasoning Models 4. Smaller, Specialized Models 5. Real-Time Learning Key Takeaways: What Every PM/Founder Needs to Remember Your Action Plan: What to Do This Week Monday: Audit Your Current Features Tuesday: Update Your Templates Wednesday: Have the Architecture Conversation Thursday: Train Your Team Friday: Adjust Your Roadmap Final Thoughts: We’re All Learning What’s Next? Let’s Keep the Conversation Going Resources & Further Reading The Moment Everything Changed Three months ago, I asked my engineering lead a simple question during sprint planning: ...