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

AI Engineer Notebooks Learn the applied-LLM stack the way you'll actually be interviewed on it — framework-free, on a free API, from prompting all the way to serving, fine-tuning, and a red-team benchmark. Runnable Colab notebooks for the AI Engineer / Forward Deployed Engineer (FDE) skill set: building working systems on top of foundation models — model APIs, RAG, evals, agents, adaptation, serving — using raw APIs, not frameworks. What makes this different Framework-free, on purpose. You write the agent loop, RAG, and evals from raw API calls first — so you understand what LangChain/LlamaIndex actually do before you reach for them (and can judge when not to). Patterns are durable; wrappers churn. Evals are the spine. "Measure before you tune" is installed early and returns in every section — the habit that separates an engineer who shipped a system from one who built a demo. Free to run, end to end. Everything runs on the free Groq API (no credit card). The two topics Groq can't host — LoRA fine-tuning (06) and self-hosted serving (09) — are concept-first with optional, fenced Colab-GPU appendices, verified on a real Colab T4. Real case studies, not toy demos. Three end-to-end case studies show the skills combined under real constraints — a support assistant debugged in production, a pipeline-vs-agent cost showdown, and a red-team robustness benchmark. OpenAI-compatible throughout, so every pattern transfers directly to OpenAI and (with small changes) Anthropic — the seam is swappable, the skills aren't. Built as the hands-on companion to Plan: Transitioning to Forward Deployed Engineer / AI Engineer. The plan explains what to learn and why; these notebooks are where you run it. Who this is for Backend or full-stack engineers moving into AI Engineer, FDE, Applied AI, or Solutions Engineer (AI) roles — different titles, largely the same job. You can ship production code; you want the applied-model layer on top. Learning order Work top to bottom. Each notebook is self-contained (installs its own dependencies, reads API keys from Colab secrets) and ends with exercises. 00 — Setup 01 — Model APIs 02 — Evals I: measuring outputs 03 — RAG 04 — Evals II: the differentiator 05 — Agents 06 — Adapting the model 07 — Security 08 — Operations 09 — Serving & inference performance Where the free Groq API can't run the topic (these frameworks need a GPU), the notebook teaches it concept-first and fences an optional Colab-GPU appendix — the same pattern as the section-06 LoRA appendix. 10 — ML system design & performance 11 — Customer craft (the FDE differentiator) 12 — Case Studies & Capstone Where the skills come together into projects. First a case study — one realistic scenario worked end to end, runnable — then the capstone, the deployed repo you build yourself. (Section overview.) Capstone: the brief for the deployed project that goes on your resume — a real repo with a serving component and an eval report. Case studies are for learning; the capstone is for hiring. Conventions Raw model APIs, no frameworks. Patterns are durable; wrappers churn. One shared corpus (data/) across RAG and eval sections, so evals measure the retrieval you actually built. Self-contained notebooks. First cell installs, second cell calls from aien import setup; client, MODEL = setup() to load your key from Colab secrets (or a local env var). No hidden state between notebooks. aien is the tiny shared-setup package in this repo — one place to change credential loading — installed automatically by the first cell. Every notebook ends with exercises — do them before moving on. Setup Get a free API key at console.groq.com — no credit card required. In Colab: the key icon in the left sidebar → add GROQ_API_KEY as a secret, and toggle notebook access on. Open any notebook via its badge and run top to bottom. Running locally instead: pip install -r requirements.txt && pip install -e . (the second installs the aien setup helper), export GROQ_API_KEY=..., open with Jupyter. Related reading Plan: Transitioning to FDE / AI Engineer — the roadmap these notebooks implement Guide: Building a Real LLM Project for Your Resume — the capstone's requirements bar Walkthrough: Designing a RAG System — the systems view of section 03 Walkthrough: Designing an AI Agent Orchestration System — the systems view of section 05