Learning Tiers Overview
The tiers represent milestones on your learning journey, not finish lines. They give us a shared vocabulary to understand where we are in our AI understanding and where we might go next.
Think of it like learning a musical instrument: saying you're at a "beginner," "intermediate," or "advanced" level helps communicate your current skills, but there's no point where a musician stops growing. The same applies here.
The Three Tiers
| Foundation | Practitioner | Expert | |
|---|---|---|---|
| Core Question | Can you understand and use AI effectively? | Can you build and deploy AI features? | Can you architect AI systems and lead others? |
| Analogy | Understanding the rules and controls | Being able to drive anywhere safely | Teaching others to drive and designing better roads |
| Primary Focus | Primitives (R1) & Compositions (R2) | Compositions (R2) & Deployment (R3) | Deployment (R3) & Emerging (R4) |
| Portfolio | 3 documented AI use cases | 1 deployed AI use case | 1 client architecture, 1 presentation, 1 mentorship |
| Assessment | Conversation with Practitioner or Expert | Technical demo + walk-through with Expert | Peer review + mentorship vouching |
What Tiers Are (and Aren't)
Tiers ARE:
- A shared vocabulary for discussing where you are in your journey
- A structured path so you know what to learn next
- Milestones that mark your growing understanding
- Portfolio builders that document your real work
- Conversation starters about AI capabilities
Tiers are NOT:
- A finish line: reaching a tier doesn't mean you stop learning
- A ranking system: Foundation isn't "worse" than Expert
- Permanent labels: skills decay without practice
- Gatekeepers: they're guides, not barriers
- One-time achievements: expect to revisit concepts
How to Think About Progression
At every tier, the same question gets asked at a deeper level: can you talk it, build it, and ultimately multiply it? This progression is the throughline of the whole journey, from explaining a concept out loud, to shipping it in real code, to making your whole team better at it.
- 1TALK ITExplain it
Can you reason about it out loud? Conversation surfaces real understanding, and filters out memorized buzzwords.
- 2BUILD ITShip it
Can you make it work in real code? Building proves the understanding holds up under contact with reality.
- 3MULTIPLY ITScale it
Can you make others better at it? Multiplying turns individual skill into team capability.
This isn't a one-way climb you finish. At every tier it runs as a loop — you reach for a deeper rung, the gaps it exposes send you back to learn and build again, and each pass leaves you with deeper understanding than the last.
Foundation isn't "Basic"
Some people assume Foundation is for beginners and they can skip to Practitioner. That's a mistake.
Foundation establishes:
- Common vocabulary we all share
- Mental models that make advanced topics easier
- Gaps you didn't know you had
- Confidence to discuss AI with anyone
Even experienced AI practitioners often discover Foundation concepts they'd misunderstood or never learned properly.
Portfolio: Proof of Understanding
Each tier includes portfolio requirements, which are documented evidence of applying what you've learned. Why?
- You learn by doing, not just reading
- Documentation reinforces learning, because explaining forces clarity
- Artifacts accumulate, and your portfolio grows over time
- Real work matters, since theory without practice is fragile
The portfolio isn't a test to pass. It's a record of your growth.
Assessment Philosophy
Assessments exist to:
- Identify gaps you can fill
- Confirm understanding of key concepts
- Provide feedback on where to focus
- Create checkpoints in your learning
Assessments don't exist to:
- Judge your worth as an engineer
- Create artificial barriers
- Measure everything that matters
- Be the final word on your capability
Think of assessments like a spotter at the gym, there to help you push further safely, not to evaluate whether you're "good enough."
The Tiers at a Glance
Foundation
"I can have an intelligent conversation about AI and use it effectively."
You understand core concepts, can use AI tools in your daily work, and can engage meaningfully in technical conversations about AI. You know the vocabulary, understand capabilities and limitations, and can make informed decisions about when to apply AI.
Key elements: Prompts, LLMs, Embeddings, Guardrails, Context Windows (conceptual), RAG (conceptual), Evaluation
Practitioner
"I can build and deploy AI-powered features in production systems."
You can independently build AI features and deploy them to production. You understand implementation details, can make architecture decisions for standard patterns, and can troubleshoot issues.
Key elements: Function Calling, Vector DBs, RAG (implementation), Multi-modal, Agents, Frameworks, Small Models, Context Windows
Expert
"I can architect AI systems, make strategic technology decisions, and advance organizational capability."
You can architect complex AI systems, make strategic technology decisions, lead AI initiatives, and elevate others' capabilities. You understand cutting-edge developments and can guide AI direction.
Key elements: Fine-tuning, Red Teaming, Multi-agent, Synthetic Data, Interpretability, Thinking Models, MCP
Getting Started
Wherever you are, start with Foundation.
Even if you've built AI systems, the Foundation tier ensures we share vocabulary and mental models. You might move through it quickly, and that's fine. Or you might discover gaps that are worth filling.