AI Engineer Notebooks: Framework-Free Colab Series Teaches RAG, Agents, Evals on Free Groq API

The AI Engineer Notebooks repo just dropped — a collection of Colab notebooks that walk you through the whole applied-LLM stack without relying on frameworks like LangChain or LlamaIndex. It's built for backend/full-stack engineers moving into AI Engineer or Forward Deployed Engineer roles. Everything runs on the free Groq API, so no credit card required.

What's Inside

The notebooks are organized into 12 sections, starting with setup and model APIs, then moving through evals, RAG, agents, adaptation, security, and serving. Key highlights:

• Framework-free by design — You write agent loops, RAG pipelines, and evaluation logic from raw API calls. The goal is to understand what frameworks like LangChain actually do before you use them.
• Evals as the spine — "Measure before you tune" is emphasized from section 02 onward, with evaluation techniques returning in every later section.
• Real case studies — Three end-to-end examples: a support assistant debugged in production, a pipeline-vs-agent cost comparison, and a red-team robustness benchmark.
• OpenAI-compatible — All patterns transfer directly to OpenAI and Anthropic by swapping the base URL.

Hands-On Notebooks

Each notebook is self-contained, installs its own dependencies, and reads API keys from Colab secrets. Early notebooks cover practical skills:

• Model APIs — Prompting fundamentals, structured output, tool calling, streaming, and context budgeting.
• Evals basics — Golden sets and metrics to start measuring output quality.
• RAG — A 15-line demo showing retrieve → augment → generate, plus embedding choice, hybrid search, and rerankers.

The repo (480 stars, 29 forks) includes 86 commits and a companion plan for transitioning to FDE/AI Engineer roles. It's a practical, no-hype resource for building production-grade AI skills.