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Level 1 · Entry Foundations

Goal: get comfortable calling an LLM from Python, engineering prompts that work reliably, wiring in tools, and shipping a small working AI assistant.

This level is about building with LLMs — you'll treat the model as a powerful API and learn the engineering patterns around it. All examples use the Anthropic API (Claude) with Python; the concepts map 1:1 to OpenAI, Google, and other providers.

Modules

  1. The LLM Landscape & Setup
  2. API Fundamentals
  3. Prompt Engineering
  4. Structured Output
  5. Tool / Function Calling
  6. Conversation State & Memory
  7. Streaming
  8. Errors, Rate Limits & Cost
  9. A Simple Agent Loop
  10. Capstone — CLI Personal Assistant

What you'll need

  • Python 3.10+ and a code editor.
  • An Anthropic API key — created in module 1. Every code snippet that talks to the API needs it; snippets that are pure Python (parsing, loops, cost math) run without one.
  • A few dollars of API credit. The whole level can be completed for well under $5 using the models suggested in the lessons.

By the end of this level you'll have built a CLI personal assistant with tools, streaming responses, conversation memory, cost tracking, and a small evaluation suite — the core skill set behind almost every LLM product.