AI Engineer Notebooks – free, framework-free RAG/agents/evals on Colab

Learn the applied-LLM stack with this series of free, framework-free Colab notebooks. These notebooks teach you what to expect in an interview, covering everything from prompting to serving, fine-tuning, and red-team benchmarking. The notebooks are designed for the AI Engineer / Forward Deployed Engineer (FDE) skill set, focusing on building working systems on top of foundation models using raw APIs, not frameworks.

The notebooks are self-contained, with their own dependencies installed and API keys read from Colab secrets. They include exercises at the end of each notebook. For topics that require a GPU, such as when using the free Groq API, the notebook includes a concept-first approach followed by an optional Colab-GPU appendix.

The notebooks cover several key skills in a realistic scenario and a real-world capstone project that you can build yourself. The capstone project features a deployed repo with a serving component and an eval report, perfect for showcasing on your resume. Running the notebooks locally is also an option, requiring you to install certain packages and set up API keys.

The notebooks focus on framework-free Colab notebooks for the AI Engineer / Forward Deployed Engineer (FDE) skill set, including topics such as model APIs, structured output, tool calling, retrieval-augmented generation (RAG), evaluation-as-the-spine, agents (from scratch, tool design, guardrails, multi-component pipelines, and skills), fine-tuning vs. LoRA, prompt-injection/security, large language model operations (LLMOps), and customer craft.