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Hereβs What You Get:
Rajiv Shah β AI Problem Framing for Agentic AI (Overview)
Instructor
- Rajiv Shah – AI Problem Framing for Agentic AI
- AI Engineer (OpenHands), professor, speaker
- 10+ years experience, 100+ real-world AI projects
- Known for teaching practical AI thinking, not just tools
Format
- Duration: ~4 weeks
- Time commitment: ~3β4 hours/week
- Format:
- Live cohort-based (2 sessions/week)
- Office hours + projects
- Lifetime access to recordings
Core Idea
Most AI projects fail not because of bad models β but because of bad problem framing.
This course teaches:
- What to build
- Whether you should build it at all
- When to pivot or stop
Instead of coding, it focuses on decision-making and thinking systems.
Key Framework: βThe Loopβ
The centerpiece is a 5-step framework:
- Outcome β What success actually looks like
- Assumptions β What must be true
- Alternatives β Other ways to solve it
- Trade-offs β Costs vs benefits
- Signals β How you know itβs working
This acts like βsystem design for AI thinkingβ
What You Learn
1. Problem Framing Mastery
- Ask the right questions before building
- Avoid solving the wrong problem
- Define success clearly upfront
2. AI Diagnostics (Super Valuable)
- Identify whatβs actually broken:
- Data?
- Model?
- Architecture?
- Or framing?
- Use diagnostic tests & signals to decide next steps
3. Decision-Making for AI Projects
- When to:
- Continue
- Pivot
- Kill a project
- Translate AI trade-offs into business terms
4. Agentic AI Thinking
- Understand when agents are the right solution
- Learn the automation spectrum (rules β full agents)
- Avoid overusing LLMs/agents
- 200+ examples of AI successes & failures
- Learn patterns behind:
- Failed chatbots
- Misused RAG systems
- Overengineered agents
Tools & Assets Included
- AI Framing Worksheet (practical tool)
- 5+ strategy canvases & checklists
- Case study library
- Weekly applied project work
Who Itβs For
Best suited for:
- AI engineers & builders
- Product managers in AI
- Founders building AI startups
- Tech leads working with LLMs/agents
Especially useful if:
- Your AI works in demos but fails in production
- Youβve wasted time building the wrong thing
- Youβre leading AI but lack strategic clarity
Pros
- Focuses on high-leverage skill (thinking), not tools
- Real-world failure-based learning
- Immediately applicable to current AI projects
- Strong for agentic AI decision-making
Cons
- No coding / hands-on building
- Expensive vs typical courses
- Abstract if you donβt have a real project
Bottom Line
This is not a βhow to build AI agentsβ course.
Itβs a:
βHow to think like an AI architect before you build anythingβ course
If most AI courses teach execution, this teaches judgment β which is rarer and often more valuable.
See More: Ashley Brock β Paid Ads Playbook Vol. II
Rajiv Shah – AI Problem Framing for Agentic AI
Name of course: Rajiv Shah – AI Problem Framing for Agentic AI
Delivery Method:Β Instant DownloadΒ (Mega)
Contact for more details: isco.coursebetter@gmail.com




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