AI for Product Management: Advanced

An advanced course for PMs, product leaders who already use AI in daily work. It moves beyond the essentials into advanced discovery synthesis, spec automation, metric-driven iteration, and AI-feature strategy, with an emphasis on quality control, automation, and judgment at scale.

What this course covers

The course runs across 10 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

AI-Feature Strategy: Portfolio Prioritization — Evaluate and prioritize high-impact AI capabilities using a strategic framework. Includes a scenario-based exercise to balance feasibility, user value, and technical risk.
Advanced Discovery: Automated Synthesis at Scale — Ingest and synthesize thousands of customer feedback points using automated LLM pipelines. Hands-on practice using discovery synthesis tools to extract core themes.
Advanced Discovery: Latent Need Extraction — Go beyond basic keyword search to identify non-obvious user pain points. Practice configuring prompt parameters in-tool to uncover latent needs in unstructured data.
Spec Automation: Structured PRD Generation — Design and execute automated workflows to generate comprehensive product specs and user stories. Hands-on exercise using AI tools to scaffold a complete feature PRD.
Spec Automation: Quality Gates & Human-in-the-Loop — Establish validation protocols and automated checks for AI-generated specs. Participate in a decision-based scenario to resolve critical hallucination risks in automated requirements.
Metric-Driven Iteration: Setting AI Guardrails — Define quantitative metrics for cost, latency, accuracy, and user satisfaction. Learn how to design telemetry to capture implicit user feedback on AI outputs.
Metric-Driven Iteration: LLM-Based Evaluation — Configure LLM-as-a-judge patterns to evaluate product performance dynamically. Hands-on lab implementing automated evaluation prompts on sample user-system interactions.
AI-Feature Strategy: Designing Feedback Loops — Architect continuous learning loops where user interactions directly improve model performance. Explore real-world case studies of feedback flywheel design.
Quality Control: Mitigating Edge Cases and Bias — Analyze toxic inputs, adversarial prompts, and system bias. Navigate a branching scenario where you must balance safety guardrails against user experience friction.
Judgment at Scale: Governing Autonomous Agents — Manage the risk, compliance, and ethical considerations of deploying autonomous agent features. Make critical governance decisions under simulated high-stakes conditions.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

Pms, product leaders. The material is pitched at advanced level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Function: Product track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including branching scenario, guided lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a lab assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

See how this training works for the whole function on AI training for Product.

Also in this track

AI for Product Management: Core — Product managers learn to use AI across discovery, specs, and prioritization. The course covers synthesizing research, writing specs and PRDs, roadmapping and prioritization, and working with metrics. Participants finish able to move from insight to plan much faster.
AI for Product Management: Essentials — Learners will be able to use AI for the everyday work of product: understanding feedback, writing things down, and keeping people informed. The course covers turning customer comments into three clear themes, drafting a one-page idea write-up, and asking AI to argue against a plan before committing. They leave with faster product habits and sharper write-ups.
AI for Product Management Pathway — Grow from everyday product habits into advanced discovery and spec automation.

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code FUN-PRD-201.