In this mini course, you'll set up a complete Python development environment for building MCP (Model Context Protocol) servers and create your first working server with tools and prompts. You'll install all necessary dependencies, configure an MCP client, and test your server interactively.

Learn how to design and run evaluation pipelines for large language models. This course covers the fundamentals of LLM evals, from writing test cases to automating scoring with model-based judges.

Retrieval-Augmented Generation (RAG) lets you ground LLM responses in your own data. In this course you'll chunk documents, embed them into a vector store, and wire up a retrieval step that feeds relevant context into your prompts.

Prompts are code. This course teaches you how to write, version, and test prompts that produce consistent results across model updates. You'll cover chain-of-thought, few-shot examples, output formatting, and common failure modes.