AI Engineer
Build products on top of large language models: how LLMs work, calling model APIs, prompting, RAG, tool use, and evals. Best if you're already comfortable with Python or JavaScript.
LLM fundamentals
What a language model is: tokens, training, inference, and why they hallucinate.
Working with model APIs
Call Claude and other LLM APIs: messages, streaming, and multimodal inputs.
Prompt engineering
Get reliable outputs with roles, examples, structure, and chain-of-thought.
Embeddings & RAG
Ground models in your own data with embeddings, retrieval, and reranking.
Tool use & agents
Let models call tools and take multi-step actions safely.
Evals & observability
Measure quality systematically instead of eyeballing outputs.
Production concerns
Handle cost, latency, caching, safety, and failure modes at scale.