Lab: AI Fundamentals for Developers
Get hands-on with the four core pillars of AI application development: models, prompt engineering, tool calling, and RAG. This lab runs entirely on your machine using Docker Model Runner — no API key or cloud account required.
Launch the lab
{{< labspace-launch image="dockersamples/labspace-ai-fundamentals" model-download="true" >}}
What you'll learn
By the end of this Labspace, you will have completed the following:
- Understand the Chat Completions API and how to structure messages for a model
- Use prompt engineering techniques including system prompts, few-shot examples, and structured output
- Implement tool calling and the agentic loop in code
- Build a RAG pipeline that grounds model responses in your own data
Modules
| # | Module | Description |
|---|---|---|
| 1 | Welcome & Setup | Introduction to the lab and verifying your environment |
| 2 | Talking to Models | Chat Completions API, message roles, and stateless model behavior |
| 3 | Prompt Engineering | System prompts, few-shot examples, and structured output |
| 4 | Tool Calling | Tool definitions, the agentic loop, and executing tools in code |
| 5 | Retrieval Augmented Generation (RAG) | Retrieve, augment, and generate with your own knowledge base |
| 6 | Wrap-up | Summary of concepts and next steps |
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