AI for Product Research & Product Development by Dr. Amar Massoud

Use generative AI across discovery, synthesis, PRDs and prototypes - with the guardrails that keep it defensible.
What you'll learn

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Map each product-lifecycle stage to the generative-AI technique that fits it, and name the stages where AI should not be trusted
Construct reusable prompt patterns with explicit role, context, constraints and output format
Run a source-verified market and competitive scan in ninety minutes, applying a two-source rule to every factual claim
Decide when synthetic data is defensible using a written routing test, and pilot survey instruments with synthetic respondents
Build an AI-assisted qualitative synthesis pipeline with full quote traceability and a 10% blind recode audit
Produce a PRD, user stories and acceptance criteria that survive adversarial AI review
Convert a written spec into a clickable, testable prototype and run a three-variant concept test in a day
Design and read a product experiment, diagnose a failing funnel, and build an eval set for an AI feature
Apply confidentiality, bias, IP, consent and EU AI Act transparency Requirements

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to your own work
Complete an end-to-end discovery-to-prototype cycle as a portfolio capstone with a 30-60-90 day adoption plan
Requirements

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No coding required and no technical prerequisites
Access to a general-purpose AI assistant (Claude, ChatGPT or Gemini)
A free-tier prototyping tool for two labs
Real data of your own - every assignment applies to your product, not the case study
Description

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This course contains the use of artificial intelligence.
Most AI-for-PM training stops at "here are twelve prompts." This course teaches theworkflow: how evidence enters an AI system, what it is allowed to decide, how the output gets verified, and what you can defend to a stakeholder, a legal reviewer, or a regulator.
You will watch a fabricated competitor price get planted in a source, pulled into a research query, repeated with a citation attached, and passed by four competent people before anybody notices. You will watch a synthetic user panel enthusiastically approve a feature that real ward managers refuse in the first ten minutes of the first session. You will watch prioritisation scores move across three identical runs. These lessons land as demonstrations and evaporate as bullet points.
What makes this different
Honest about synthetic data. An entire section on where generated respondents are defensible and where they are not, backed by the finding that around 97% of researchers now use AI somewhere while only about 8% regularly use synthetic-respondent tools.
Traceability as a discipline. Every insight links to a real quote with a source ID. Every market claim carries two independent sources.
Regulation that is current. What binds you now under EU AI Act Article 50, versus what moved to December 2027 and August 2028.
A single continuous case. Brightline Software, a 480-person B2B healthcare scheduling company, carries a story that moves forward every section - from an eleven-week cycle to three weeks.
What you walk away holding
15 downloadable resources arriving at the lecture where you first need them: a 12-prompt library, market scan and teardown templates, a source verification log, the synthetic data decision test, interview guide and coding rubrics, an annotated PRD template, adversarial review prompts, a prototype brief and concept test script, an experiment worksheet, an eval-set starter, a prompt data red-list, an AI use policy template, an EU AI Act checklist, and the capstone workbook. Plus 10 quizzes, 9 assignments, a 30-question final practice exam, and a portfolio-grade capstone.
Who this course is for

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Product managers who want AI in the workflow rather than in the demo
UX and market researchers scaling qualitative work without losing traceability
Product leaders who need to answer "where did that number come from?"
Homepage

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