Evals aren't enough, Learn the power of reframing AI problems
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 1 – June 26 🚨
Use Code: Earlybird (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
🛠️ Bring your own AI Agent project and apply the frameworks each week (or use a provided case study)
🧑🏫 2 Live sessions per week plus office hours
📊 Access to 200+ case studies, the largest collection of AI reframing examples
📋 Additional reference material covers traditional ML and Gen AI use cases
🎥 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 problem 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.
Student or between jobs? Message me. Financial assistance is available
What you’ll learn
How to think through AI Agent problems end-to-end: scoping, debugging, and knowing when to pivot.
Use the Loop: a 5-step framework (Outcome, Assumptions, Alternatives, Trade-offs, Signals) for your AI solutions
Learn to ask the questions that reveal whether you're solving the right problem.
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.
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."
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.
Who this course is for
Engineers who can build AI but want to master outcome engineering: knowing what agent to build, not just how
Building agents, RAG pipelines, or ML models? Let's get your demos into production! Learn the thinking that makes the difference.
Managers who need to evaluate AI project proposals, set realistic milestones, and know when to escalate
Prerequisites
You understand what AI/ML is at a conceptual level.
We focus on framing and strategy, not explaining what models or training means. Basic vocabulary lets us go deeper faster.
You've worked on or led AI projects in some capacity.
Real experience gives you context. The frameworks click when you can map them to projects you've actually built or managed.
No coding during the course. Its about frameworks for thinking, not tutorials.
This is about decision-making, not implementation. You'll learn what to build and why, not how to code it.
What's included
Live sessions
Learn directly from Rajiv Shah in a real-time, interactive format.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
8 live sessions • 62 lessons • 6 projects
Week 1
Lesson 1: Broadening your Agentic Mindset
Bonus: AI Alternatives for Gen AI and Traditional ML
Jun
2
Optional: Lesson 1 Review + Live Office Hours 1
Jun
5
Optional: Lesson 1 Review + Live Office Hours 2
Week 2
Lesson 2: The Loop - A Framework for AI Problem Framing
Jun
9
Optional: Lesson 2 Review + Live Office Hours 3
Jun
12
Optional: Lesson 2 Review + Live Office Hours 4
Free resource
AI Framing Worksheet
Most AI projects fail because the problem was set up wrong, not because the model was bad. The AI Framing Worksheet is a one-page tool with 8 questions across three sections:
- Problem — what are we actually solving?
- Approach — what's the simplest thing that works?
- Evidence — how will we know it worked?
plus three bonus diagnostic tests:
- Oracle Test
- Unit Test
- Simplicity Test
The quickest way to surface issues and get your AI shipped to production.
Schedule
Live sessions
2 hrs / week
Combination of live sessions covering material and office hours for your questions. I have setup time zones to accommodate students in many time zones. You should need to attend one live session a week. Please make sure the time is acceptable to you.
Tue, Jun 2
1:00 PM—2:00 PM (UTC)
Fri, Jun 5
1:00 AM—2:00 AM (UTC)
Tue, Jun 9
1:00 PM—2:00 PM (UTC)
Projects
1 hr / week
Each week you'll apply the frameworks to a real project. Bring one you're actively working on for the most value. If you're between projects, we'll provide a detailed case study you can use throughout the course.
Recorded Lessons
1-2 hrs / week
Lectures are recorded for your convenience
Voices from the field
"The key question for enterprise AI is: what problem are you actually solving?" -- Alexandru Vesa, MLOps Engineer
"Tools go in and out of style quickly, but fundamental approaches to problem solving should last a bit longer." -- Chip Huyen, Stanford ML Systems Design
"The future of work is all of us becoming managers of AI." -- Richard Socher, former Chief Scientist at Salesforce
Course Map
The Outer Loop for Reframing Your AI Projects
What You will Learn
The Strategy & Mindset
- The 5-Step Loop: A structured engineering lifecycle for deconstructing and reframing complex AI Agent problems before writing code.
- The Automation Spectrum: Four levels from rules to full agent.
- 3 Thinking Methods: Inversion, De-escalation, and the Feynman Test. Break System 1 pattern-matching and catch bad framing before it becomes expensive.
The Diagnostics
- 6 Diagnostic Tests: Figure out whether your agent problem is model capability, orchestration, task framing, or an alternative approach.
- Failure Funnel: Identify where errors are happening.
- 6 Pivot Signals: A "Smell Test" for recognizing when an architecture has hit a dead end (and exactly what to do about it).
The Toolkit
- 5 Canvases & Checklists: Course-exclusive worksheets for running strategy sessions, including the AI Problem Framing Questionnaire.
The Reference Library
- 200+ Case Studies: A searchable database of real-world pivots (Netflix, Uber, Stripe) mapped to the 18 architectures in the course.
Participation & Code of Conduct
Participation Expectations
This is a practitioner-focused cohort built on trust and discussion.
Participants are expected to engage respectfully and professionally.
Disruptive behavior, bad-faith participation, or misuse of course materials may result in removal.
Content Use Policy
Course content, discussions, slides, and materials are for personal educational use only.
Recording, transcribing, scraping, or redistributing any portion of the course is not permitted.
This includes sharing with employers or third parties.
Violation may result in removal from the course.
If you have any questions, reach out. I spend a lot of time preparing and want to make sure we have a conducive environment for learning.
Frequently asked questions
$980
USD