Over the last few weeks, I’ve been running a series of live sessions for PMs, engineers, and AI-curious builders who want to go beyond buzzwords and actually reason about AI products.
We focused on three themes:
🧩 1. Deconstructing RAG
In this session, we broke RAG down into simple, practical pieces:
- A clear retrieval + generation mental model for more reliable AI products
- Core building blocks: embeddings, chunking, vector stores, retrievers, and the end-to-end flow
- How to decide when RAG is the right answer – and when it isn’t
- The PM’s role in RAG quality: metrics, hallucinations, and experiments
- Real‑world constraints: latency, cost, data quality, context limits, and common pitfalls we see in the wild
🤖 2. Getting Started with AI Agents
In this webinar, we covered:
- What an AI agent really is, and how it differs from a traditional app or “just a chat UI”
- Agent architecture 101: tools, memory, reasoning loops, LLM orchestration
- How to think about frameworks like LangChain
- A step‑by‑step path to your first working agent: define use case → design → build → deploy
- Common pitfalls first‑time builders run into, and simple patterns that actually ship
🎯 3. Breaking into AI Product Management
AI PM interviews today look very different from “generic PM” loops.
- How AI PM interviews differ from traditional PM interviews
- The typical formats used by startups and larger tech companies
- The level of AI & GenAI understanding PMs are expected to demonstrate (without being ML researchers)
- How to structure answers across product sense, execution, and AI trade‑offs
- Common mistakes candidates make – and what strong, nuanced answers look like
We also ran live mock interview with participants and shared real‑time feedback on problem structuring, communication clarity, and AI/product depth. Watching these breakdowns gave people a “behind the scenes” view into how interviewers actually think.
🔭 What I’m seeing across all three
Across RAG, agents, and AI PM interviews, a pattern keeps repeating:
> People don’t just need more content on AI.
> They need better mental models and feedback on how they think about AI products.
That’s the gap I’m trying to close through my work – combining AI literacy, product thinking, and career growth for PMs, aspiring PMs, and technical professionals.
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👥 What’s next – AI PM Cohort
To go deeper into this, I’m also running an AI Product Management cohort for aspiring and current PMs who want structured practice, real product scenarios, and feedback.
If you’re exploring a move into AI/GenAI product roles or want to strengthen your decision‑making as a PM, this cohort is designed to give you that combination of concepts, frameworks, and hands‑on application.
Details on structure, schedule, and pricing are here:
https://lnkd.in/gwMX-v-A
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