The 4-Step AI Prompt Framework Every Product Manager Should Know

What does it mean to prompt AI the product manager way? In this podcast hosted by Hoda Mehr, Co-founder and CEO of Up My Mojo and a Board Member at Products That Count, Ridge Ventures Venture Partner Akriti Dokania will be sharing the 4-Step AI prompt framework every product manager should know. Akriti brings a rare perspective, having worked as an engineer and product manager at Amazon and Microsoft and now serving as a venture partner and board member to several successful B2B startups.

The conversation was sparked by an article Akriti recently published on how to prompt AI models the product manager way, an idea that reframes the current AI conversation. While much of the industry focuses on what AI models can do, Akriti argues that the real variable is the person using them: how the problem is framed, how the conversation is structured, and whether the user knows what kind of help they are actually looking for.

Together, they go deep on practical frameworks product builders can use immediately, the mindset shifts that separate those who get real value from AI from those who stay in the shallow end, and what Akriti’s experience across engineering, product management, and venture investing reveals about the difference between product builders who are truly AI-native and those who are simply using AI.

Join us for new conversations with leading product executives every week. Roll through the highlights of this week’s event below, then head on over to our Events page to see which product leaders will be joining us next week.

Show Notes:

1. AI is evolving at extreme speed, making experimentation the core advantage for product managers today.
2. Product managers are uniquely positioned to use AI effectively because they think in terms of outcomes and user impact, not just inputs.
3. The key shift in prompting is moving from commands (“do this”) to context (“here’s the situation”).
4. Rich context leads AI to ask better clarifying questions, which improves output quality significantly.
5. A strong prompt often emerges through iteration—using AI itself as a brainstorming partner to refine prompts.
6. Assigning AI a specific role (e.g., assistant, critic, coach) dramatically changes the depth and usefulness of responses.
7. Without a role, AI responses tend to stay generic; with a role, they become interactive and goal-oriented.
8. Product managers naturally excel at prompting because they are trained to be context-seekers and truth-seekers.
9. The best prompts mirror real human collaboration—how one would brief a teammate or ask a mentor for help.
10. One of the most powerful techniques is explicitly naming risks and challenges upfront.
11. AI performs better when it understands what could go wrong, not just what success looks like.
12. There’s a difference between assumptions and risks—risks are the real constraints that can break the solution.
13. Let AI identify challenges first, then layer in your own—this avoids biasing the system too early.
14. The most advanced users consistently “question the question”—challenging AI’s answers to go deeper.
15. Asking follow-ups like “Are you sure?” or “What am I missing?” unlocks more nuanced insights.
16. AI is optimized to complete tasks, so continued questioning pushes it toward higher-quality outputs.
17. There’s a clear maturity curve in AI usage: Surface-level use (emails, summaries), building workflows/agents, reflecting on limitations and iterating deeply
18. Being “AI-native” means building and experimenting constantly, not just understanding concepts.
19. A critical differentiator is knowing what doesn’t work and how to work around limitations.
20. The biggest mindset shift: ship something—even a small automation—because hands-on building accelerates learning far more than theory.Tell me