AI For Product Managers
Enough model understanding to lead with confidence, without becoming a machine learning engineer.
You do not need to build models, but you do need to reason about them. This track covers the handful of ideas that shape what an AI product can do: how language models work, why retrieval matters, and the accuracy, latency and cost tradeoffs behind every AI feature decision.
Guides and tools
Everything in the AI & Model Fluency track. Live pages open now, more are on the build roadmap.
LLMs and RAG for product managers
The two ideas that shape most AI products, explained without the maths.
Read guide →Embeddings, simply explained
What embeddings are and why they power search and recommendations.
In progressFine-tuning vs prompting vs RAG
Which approach to reach for, and when each is worth the cost.
In progressAccuracy, latency and cost
The triangle behind every AI feature tradeoff, in practice.
In progressData literacy for AI PMs
Reading model behaviour and data quality without an engineer translating.
In progressCommon questions
Frequently asked questions
Do I need a technical background for this track?
No. It is written for product people. It gives you the working mental model of how models behave, which is enough to make product decisions and talk credibly with engineers, without any coding.
Why does model fluency matter for a PM?
Because AI product decisions, like which model to use or what quality bar to set, depend on understanding tradeoffs. Fluency lets you make those calls yourself rather than deferring every one to engineering.
What should I read first here?
Start with LLMs and RAG for product managers. It sets up the core vocabulary and mental model that the other guides in this track build on.