Maven – AI Problem Framing for AI Practitioners

Most AI fails from bad framing, not bad models. Learn to fix that.

AI Problem Framing is to AI teams what System Design is to software engineers and Product Sense is to PMs. Whether you’re building, evaluating, or leading AI work, this is the foundational thinking skill that separates results from rework.

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? Summer cohort: June 2 – June 26 ?

Winter Cohort is finished

Use Code: Earlybird for 20% discount if you sign up by April 30th

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What you get
? The Loop, a 5-step framework you’ll use to think through every AI project

?‍? Live sessions and office hours where you can bring your real problems

? Access to 200+ case studies, the largest collection of AI reframing examples

? Production-ready checklists for RAG, Forecasting, GenAI

? Lifetime access to all recordings

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This course is for you if
• Your AI works in demos but fails in production

• You’ve spent months on a model only to realize you solved the wrong problem

• You lead AI work but came from engineering, product, or PM

You’ll learn from 200+ AI failures in 4 weeks. You gain years of experience and recognize the scars.

If you are a student or between jobs, send me a note about your current situation and why this course is important to you, and I may discount the course appropriately.

What you’ll learn
How to think through AI problems end-to-end: scoping, debugging, and knowing when to pivot.

Frame AI problems right the first time

Use the Loop: a 5-step framework (Outcome, Deconstruction, Alternatives, Trade-offs, Signals) for your AI solutions

Learn to ask the questions that reveal whether you’re solving the right problem.

Diagnose what’s actually broken

Recognize the signals that tell you what’s actually broken.

Learn whether to fix the data, fix the architecture, or fix the framing, and how to tell the difference.

Own the problem, not just the solution

Shift from executor to AI Architect: question requirements before building them.

Push back with evidence: “I know you want a chatbot, but here’s why search is better.”

Steer your team past expensive mistakes

Study real failures so you can spot the warning signs before they become expensive.

Set realistic expectations and catch bad framings before the team spends months on them.

Translate AI trade-offs into business terms stakeholders actually understand.

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