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 production feature shipped | 1 architecture led + mentorship |
| Assessment | Written portfolio + review | Technical demo + code review | Architecture presentation + peer review |
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
The Learning Cycle
┌──────────────────────────────────────┐
│ │
▼ │
LEARN APPLY TEACH │
concepts ───▶ in work ───▶ others ───┘
│
▼
DEEPER UNDERSTANDING
Each tier involves all three phases:
- Learning new concepts from the periodic table
- Applying them in real projects (your portfolio)
- Teaching or explaining to others (reinforces your own understanding)
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—documented evidence of applying what you've learned. Why?
- You learn by doing, not just reading
- Documentation reinforces learning—explaining forces clarity
- Artifacts accumulate—your portfolio grows over time
- Real work matters—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, 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 (advanced), 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—that's fine. Or you might discover gaps that are worth filling.