GoKawiilAI 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.
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.
"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 .
Everything runs on the (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, . 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.
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.
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