AI Product Manager
AI Product Managers sit at the intersection of product ownership and AI capability. They define AI feature requirements, work with data scientists on evaluation metrics, manage the responsible AI aspects of product launches, and translate model uncertainty into product decisions. Their English work includes writing AI feature PRDs, presenting model metrics to executives, and communicating AI limitations to users and partners. This path builds the vocabulary and communication patterns specific to AI product management.
Topics covered
- AI product strategy
- Model evaluation for PMs
- Responsible AI principles
- AI feature lifecycle
- Stakeholder communication for AI
- AI roadmap language
Vocabulary spotlight
4 terms every AI Product Manager should know in English:
A confident but factually incorrect output from a language model — the model generates plausible-sounding but invented information
"We added a citation requirement to the feature to reduce hallucination risk and build user trust."
A constraint or filter applied to an AI model's inputs or outputs to prevent harmful, off-topic, or policy-violating responses
"The legal team required guardrails that prevent the assistant from giving specific financial advice."
A quantitative measure used to assess an AI model's performance on a specific task — chosen to reflect the user value or business goal the model is meant to deliver
"We moved from accuracy to task completion rate as our primary evaluation metric because it better reflects user success."
The reduction in capability or helpfulness that results from applying safety and alignment constraints to an AI model
"We accepted a small alignment tax in exchange for eliminating the most common user-reported harmful outputs."
📚 Vocabulary Reference
Key terms organised by category for AI Product Managers:
AI Product Concepts
Model Evaluation for PMs
Responsible AI
AI PM Communication
Recommended exercises
Real-world scenarios you'll practise
- Writing an AI feature PRD: specifying evaluation criteria, guardrail requirements, and fallback behaviour for an LLM-powered feature
- Presenting AI model metrics to the executive team: translating accuracy, precision, and recall into business impact language
- Running a responsible AI review: facilitating a cross-functional discussion on bias, fairness, and harm risks for a new AI feature
- Communicating model limitations to users in product copy: explaining confidence levels, potential errors, and when to verify outputs