September 29, 2025 by
I’m starting a new role in a few weeks and in the interim I wanted to structure some learning around product and AI alongside making some time for gardening and learning the piano. I asked Claude to generate a plan that I can follow and this is what it provided. I expect to loosely follow the approach and document it here on the blog.
Week 1: AI Foundations & PM Fundamentals
Learning Focus: Understanding the AI product landscape and current best practices Hands-on Projects:
Days 1-2: Build a simple chatbot using Claude API or OpenAI API. Create a basic use case (customer support, content assistant, etc.) to understand prompting, API integration, and user experience considerations.
Day 3: Explore prompt engineering - create a prompt library for different use cases and document what works/doesn’t work.
Day 4: Study and document 3 AI products you admire. Analyze their product strategy, user flows, and monetization.
Day 5: Create a mini PRD (Product Requirements Document) for an AI feature you’d add to an existing product
Resources to explore:
- Anthropic’s Claude documentation and prompt engineering guide
- Lenny’s Newsletter recent AI PM content
- Try multiple AI tools hands-on (Claude, ChatGPT, Perplexity, Cursor)
### Week 2: AI Product Development & Metrics
Learning Focus: Building, measuring, and iterating AI products Hands-on Projects:
Day 1: Design and prototype an AI feature using Figma or similar tool. Focus on the user journey and edge cases.
Day 2: Learn about RAG (Retrieval Augmented Generation) - build a simple document Q&A system using your own documents.
Day 3: Create a metrics framework for an AI product. Define success metrics, quality metrics, and guardrails
Day 4: Conduct “user **research” by testing your Week 1 chatbot with 3-5 people. Document feedback and iterate.
Day 5: Write a one-pager on AI product strategy for a domain you’re interested in
Key concepts to study:
- LLM evaluation and testing
- Responsible AI and safety considerations
- Token economics and cost optimization
- AI product-market fit indicators
Week 3: Advanced Topics & Portfolio Preparation
Learning Focus: Emerging trends and synthesizing your learning Hands-on Projects:
Day 1: Explore AI agents - build a simple multi-step agent that can perform a task (research assistant, data analyzer)
Day 2: Study multimodal AI - experiment with image/document analysis APIs
Day 3: Create a competitive analysis of AI products in a specific vertical (healthcare, education, legal, etc.)
Day 4: Develop a pitch deck for an AI product idea, including market analysis, user personas, and roadmap
Day 5: Consolidate your learning - write a reflection document and update your LinkedIn/portfolio with your projects