FAQs: AI-First Product Management

No coding required, though comfort with basic technical concepts helps.

Primarily, but founders and designers making AI product decisions get value too.

No-code prototyping tools plus OpenAI/Anthropic APIs for hands-on exercises.

Yes — you'll scope and prototype a real AI feature by the end of the cohort.

₹16,999 for the full 6-week Intermediate-level cohort, including hands-on prototyping tools and API credits used in class exercises.

Blogs and free frameworks explain concepts in the abstract; this course has you scope, prototype and ship an actual AI feature with instructor feedback, which is the part self-study can't replicate.

No coding is required — this is an Intermediate-level PM course, so basic product management experience helps more than technical depth.

Yes, you receive a certificate of completion along with your capstone case study, which is the stronger proof of skill for job applications.

Sessions are recorded with lifetime access, and the capstone timeline has built-in buffer weeks so you can catch up without missing the final project deadline.

Graduates use this to move into AI-focused PM roles, lead AI feature launches at their current company, or make sharper build-vs-skip decisions as a founder — the capstone case study is designed to support all three paths.

No — the core skill is knowing enough about AI capabilities and limitations to make sound product decisions, not building models yourself.

AI-first PM requires handling probabilistic feature behavior and trust calibration, beyond the deterministic frameworks traditional PM relies on.

Yes — participants build a complete AI feature product plan applying everything learned throughout the course.

Product managers leading AI initiatives, founders evaluating AI fit, and engineers transitioning into AI-focused product roles.

Yes — designed to demonstrate product judgment on AI feature decisions that a hiring manager or promotion committee can evaluate concretely.

No — the course teaches enough technical fluency for sound product decisions without requiring you to become a practitioner.

Yes — direct experience shipping AI-powered features in real products, not just academic knowledge.

A combination — live sessions for discussion and feedback, with self-paced material for foundational content.

Yes, to make the course accessible to a broader range of professionals.

Yes — an alumni community remains accessible for practical questions encountered while leading AI initiatives on the job.

Yes — reviewed regularly to reflect current model capabilities rather than teaching outdated assumptions.

Yes — for upskilling multiple product managers, with the option to adapt case studies to the company's specific industry.

Yes — with equivalent content and interaction quality in both remote and in-person formats.

AI-specific depth and uncertainty handling are the core focus, adding genuinely new skills beyond general PM foundations.

Dedicated AI product manager roles, leading AI initiatives within existing teams, or advising on AI opportunities as a founder.

Practical judgment — the skills needed to ship AI features responsibly, rather than inspiring but abstract vision.

Yes — woven throughout as practical product decisions like bias and transparency, not treated as a separate abstract topic.

Yes — many participants use their capstone to work through a real current challenge, receiving feedback with immediate practical application.

Yes — additional foundational material ensures non-technical participants can engage fully with technical discussions.

Yes, reviewed regularly to reflect current model capabilities rather than outdated assumptions.

Yes — anonymized real case studies of AI features that succeeded and failed give concrete examples of what separated the two outcomes.

Yes — practical frameworks for honestly representing both opportunity and genuine uncertainty when pitching are taught.

Some prior professional experience is recommended, though the course accommodates a range of backgrounds and experience levels.

Yes — while core concepts remain consistent, examples and the capstone can be tailored to your specific product context.

Yes — a recurring shift from thinking in terms of specific features to user outcomes an AI capability could genuinely improve.

Yes — continued feedback helps participants refine their project further for actual interviews or promotion conversations.

Discounted pricing options are available in certain circumstances — reaching out directly to discuss individual circumstances is recommended.

The capstone project is a core requirement for certification, since it's where the practical skills genuinely solidify.

The certificate is designed to demonstrate concrete, verifiable skill via the capstone portfolio rather than relying on name recognition alone.