ai-engineer-notebooks

Why we included this project

Engineers moving into applied LLM work will find a structured, hands-on curriculum here rather than a deployable tool. The notebooks build each piece from raw API calls first, deliberately skipping frameworks, so you see what LangChain and LlamaIndex actually do under the hood and when to leave them out. Everything runs on the free Groq API with no credit card, and the two topics Groq can't host, LoRA fine-tuning and self-hosted serving, come with optional GPU appendices verified on a real Colab T4. Three end-to-end case studies tie the skills together under real constraints, including a support assistant debugged in production and a red-team robustness benchmark. Backend and full-stack engineers preparing for AI Engineer or Forward Deployed Engineer roles get a practical, interview-aligned way to build working systems and the measurement habits that separate shipped systems from demos.