Tier 1: Foundation
"I can have an intelligent conversation about AI and use it effectively."
What This Level Means
Foundation demonstrates that you understand the core concepts of modern AI, can use AI tools effectively in your daily work, and can engage meaningfully in technical conversations with colleagues and clients.
You know the vocabulary. You understand capabilities and limitations. You can make informed decisions about when and how to apply AI.
This isn't about being "basic"—it's about having a solid foundation that everything else builds on. Many experienced engineers discover gaps at this level they didn't know they had.
Elements to Explore
These are the concepts to understand at Foundation. You don't need to master implementation details—that's Practitioner level. Focus on understanding what these are, why they matter, and when they apply.
| Element | Concept | What to Understand |
|---|---|---|
| Pr | Prompts | How to write effective prompts with clear instructions, context, examples, and constraints. Understand prompt patterns and iteration. |
| Lg | LLMs | What LLMs are, how they work at a high level, their capabilities, limitations, and hallucination risks. |
| Em | Embeddings | How semantic similarity works conceptually and why it matters for AI applications. |
| Gr | Guardrails | AI safety, bias, ethical considerations, and why guardrails matter in production systems. |
| Rg | RAG | The RAG pattern conceptually: retrieval, augmentation, generation. Know when and why to use it. |
| Ev | Evaluation | How AI quality is measured. Common metrics, benchmarks, and the importance of human evaluation. |
Portfolio: Document Your Learning
As you learn, document 3 real work tasks where you used AI effectively. This isn't busy work—it's how learning becomes lasting.
For Each Use Case, Include:
1. The Task or Problem What were you trying to accomplish? What was the context?
2. Your Approach
- What AI tool(s) did you use?
- How did you structure your prompts?
- What iterations did you go through?
- What didn't work at first?
3. The Outcome
- How did AI contribute to the result?
- What would have been different without AI?
- Was there anything AI did poorly?
4. What You Learned
- What would you do differently next time?
- What concepts from the periodic table were in play?
- Did you discover any gaps in your understanding?
Portfolio Examples
Good portfolio entries might include:
- Using AI to draft technical documentation, showing prompt iteration
- Analyzing code with AI assistance, noting where it helped and where it hallucinated
- Having AI explain an unfamiliar codebase, documenting the back-and-forth
- Using AI to debug an issue, tracking the conversation flow
Use the Foundation Portfolio Template to structure your documentation.
Skills to Develop
Prompting
Can you:
- Write clear, structured prompts with role, context, task, and constraints?
- Use few-shot examples effectively?
- Iterate on prompts when results aren't what you need?
- Recognize when a prompt problem vs. a model limitation is the issue?
LLM Understanding
Can you:
- Explain what an LLM is to a non-technical person?
- Describe common LLM limitations (hallucination, knowledge cutoff, etc.)?
- Identify appropriate vs. inappropriate use cases for LLMs?
- Understand why the same prompt might give different results?
Conceptual RAG
Can you:
- Explain the RAG pattern at a whiteboard level?
- Describe when RAG is useful vs. when fine-tuning might be better?
- Understand how embeddings enable semantic search?
Safety Awareness
Can you:
- Identify potential risks in a proposed AI application?
- Explain why guardrails matter in production?
- Discuss AI bias and ethical considerations?
Assessment Approach
Foundation assessment includes:
Written Component
Demonstrate understanding of core concepts. This isn't a gotcha test—it's a conversation about your understanding. Expect questions like:
- "Explain how you'd approach using AI for X task"
- "What are the risks of Y approach?"
- "When would you choose RAG vs. fine-tuning?"
Portfolio Review
Walk through your documented use cases with a Practitioner or Expert. They'll ask about your reasoning, what you learned, and how you might approach it differently now.
What "Passing" Means
Assessment confirms you've built a solid foundation. If there are gaps, that's valuable information—fill them and revisit. The goal is genuine understanding, not a score.
Learning Path Suggestions
Start Here
- Read through the AI Periodic Table overview
- Deep dive into Prompts and LLMs
- Start documenting AI use in your daily work immediately
Build Understanding
- Explore Embeddings and RAG
- Study Guardrails and Evaluation
- Practice explaining concepts to others
Solidify
- Complete your 3 portfolio use cases
- Review your understanding against the skills checklist
- Schedule your assessment when ready
Common Questions
Q: I've been using ChatGPT for years. Can I skip Foundation?
Go through Foundation anyway. You might move quickly, but many experienced users discover conceptual gaps. Plus, you'll establish the shared vocabulary we use across tiers.
Q: How long does Foundation take?
It varies. Some people move through in a few weeks; others take longer. Don't rush—the foundation you build here supports everything that follows.
Q: What if I fail the assessment?
You get feedback on gaps, fill them, and try again. It's not a judgment—it's information about where to focus.
Q: Do I need to memorize everything?
No. Understanding matters more than memorization. You can reference documentation. The goal is knowing what to look for and how concepts connect.
What's Next?
After Foundation, you'll have a solid mental model of the AI landscape. Practitioner builds on this foundation with hands-on implementation skills.
Ready to Start?
- Review the periodic table elements above
- Begin documenting your AI use immediately
- Use the portfolio template
- Connect with others on the same journey