Question 1

What does an AI Product Manager actually do (and how do you work)?

Accepted Answer

I bridge cutting-edge models with business outcomes: define the problem, prove feasibility fast, ship value, then scale responsibly. My approach blends user research, LLM evaluation, and lean experimentation. I led Red Bull’s first global GenAI product and delivered $12M ARR uplift for Google Ads by owning experimentation at scale.

Question 2

How do you take an AI idea from 0→1?

Accepted Answer

Start narrow and validate quickly with interviews, data, and prototype prompts. I convert insights into a PRD with example inputs/outputs and measurable acceptance criteria. This delivered a 30% view-to-activation conversion on v1 of Red Bull’s AI product.

Question 3

How do you measure and improve generative AI quality?

Accepted Answer

I implement an LLM evals framework tracking task accuracy, safety, UX fit, latency, and cost. At Red Bull, automation improved iteration speed 4× and enabled safe A/B releases tied to product and business KPIs.

Question 4

What results have you driven at scale?

Accepted Answer

Scaled Red Bull’s GenAI product from 0→70K users across 40 countries (20× YoY). At Google Ads/AdSense, delivered $12M ARR uplift and maintained platforms serving 1M+ publishers. Reduced invalid applications by 21% via safety and integrity systems.

Question 5

How do you keep AI products compliant, safe, and brand-aligned?

Accepted Answer

I partner early with legal, security, and brand to define guardrails (policy filters, PII handling, content moderation, human-in-the-loop) and ship with clear disclosure and auditing for global scale.

Question 6

Which models and partners have you worked with?

Accepted Answer

I’ve collaborated with Microsoft, AWS, and Suno research teams on training and evaluation, selecting models based on quality, latency, cost, data boundaries, and the target experience.

Question 7

What’s your playbook for AI infra and cost optimization?

Accepted Answer

Right-size model choice, cache, batch, monitor token and latency budgets, and gate high-cost paths to highest-value flows. I’ve owned AI infra budgets and tuned usage to maintain quality with healthy unit economics.

Question 8

How do you work with founders and hiring managers?

Accepted Answer

For founders: validate use cases, build a lean MVP, and install evals/experiments to find traction. For hiring managers: lead cross-functional roadmaps, align execs on measurable outcomes, and de-risk launches with staged rollouts and A/B tests.

Question 9

Can you share frameworks or talks?

Accepted Answer

See site sections for case studies and articles (About, Work, Insights). I share 0→1 scoping, evals, safety, and scale frameworks; talks and articles are linked when available.

Question 10

What’s your view on the future of LLM products?

Accepted Answer

Products will evolve into collaborative agents with oversight. Winning teams design behavior (prompts, tools, memory), trust (safety, transparency), and unit economics (quality × latency × cost). I operate at that intersection.

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