You've built AI and know it can go better. A structured process for framing the problem, diagnosing what breaks, and deciding when to change course.
| Where you are | Start with |
|---|---|
| Scoping a new AI project | The framework, then the AI Framing Worksheet. |
| Debugging a stuck project | Why framing matters, then the Diagnose stage. |
| Working on traditional ML | The ML section in the framework. |
| Working on GenAI or RAG | The GenAI section in the framework. |
| Working on agents | The Agents section in the framework. |
| Curious about the thesis | Why framing matters, then the Ship Your AI Project webinar. |
If you've shipped an AI project, you already know the initial framing was the easy part. The hard part came later: the model shipped and the signals said something was off, the demo frame did not survive production, the benchmark looked good and the users did not. Most AI guides stop before that. This site is about what comes after.
The course covers framing across traditional ML, GenAI, and agents, with worked projects for each. The three stages are the same in every case. The strategies inside each stage are not. I bring specific strategies for each class, drawn from years of working on AI projects across all three. See how it cuts across all of AI →
The Maven cohort runs four lessons over four weeks: the problem, the loop, the diagnosis, the pivot. The next cohort runs September 2026, and early-bird pricing is open now. If this piques your interest, feel free to reach out and chat more.
Rajiv brings rigor and relevance together, while avoiding the AI hype and focuses the course on helping you frame problems in a way that make them suitable for agents and then evaluating performance and also deciding when to discontinue projects. Strongly recommend.
— Vishal Sachdev, Professor, University of Illinois at Urbana-Champaign
Students rate the course 4.9 on Maven. Read more reviews →