Owns AI feature strategy + roadmap, evaluation, ROI analysis, vendor selection, success metrics.
Understand what LLMs can and can't do: capabilities, limitations, cost structures, and evaluation.
By the end of this week, you can defend the cost and latency floor of a feature using real token math.
PMs who quote token costs and latency floors win trust with engineering. Vague AI literacy gets caught fast.
Watch out: PMs who promise 'we'll add AI' without a token budget get caught in five minutes. Bring numbers to every roadmap conversation.
When to use RAG vs fine-tuning vs prompting. Map business problems to AI solution patterns.
By the end of this week, you can pick prompting vs RAG vs fine-tuning for a real product brief and defend the choice.
The pick-the-right-tool framing is the single most common AI-PM interview prompt. Walking the trade-off matrix beats name-dropping LLMs.
Watch out: Reaching for fine-tuning when prompting would work is the most expensive PM mistake. Always start with the cheapest option that meets the bar.
AI product strategy, vendor evaluation, ROI analysis, and responsible AI practices.
By the end of this week, you can design an offline plus online eval plan for any AI feature you would ship.
How you measure an AI feature's win or loss reveals whether you can defend roadmap bets. Eval design questions are the senior filter.
Watch out: Vague 'we'll measure user love' answers fail PM interviews. Be specific about the metric and the threshold.