# Track 00: GenAI Foundations

Before touching an API or a library, get the concepts straight: what
"generative AI" actually means, how the field arrived at today's large
language models, what these systems can and can't reliably do, and how
the pieces you'll learn in later tracks (tokens, transformers, RAG,
agents, MCP) fit into one coherent stack. This track is conceptual by
design — the only code is your environment setup at the end.

## Modules

1. [What Is Generative AI (and What It Isn't)](00-what-is-generative-ai/README.md)
2. Discriminative vs. Generative Models
3. A Brief History: Symbolic AI to Statistical NLP
4. Word2Vec and the Embedding Revolution
5. RNNs, LSTMs and the Sequence Problem
6. The Attention Breakthrough
7. The Transformer Moment (2017)
8. The Scaling Era: GPT-1 to ChatGPT
9. How LLMs Actually Generate Text
10. What LLMs Are Genuinely Good At
11. Limitations: Hallucination, Knowledge Cutoff, Reasoning
12. The Modern GenAI Stack
13. What "AI Engineer" Actually Means
14. Setting Up Your Environment (Python, API Keys, Cost Guardrails)
15. Capstone Project