AI Engineer Notebooks – free, framework-free RAG/agents/evals on Colab (github.com)

AI Engineer Notebooks has introduced a set of free, framework-free Colab notebooks designed for aspiring AI engineers and forward-deployed engineers (FDEs). These resources focus on the applied low-level model stack, encompassing essential skills from prompting and serving to fine-tuning and benchmarking. By using raw API calls, learners can gain a deeper understanding of how various frameworks like LangChain and LlamaIndex function, enhancing their ability to develop and evaluate working systems with foundation models. This initiative is significant for the AI/ML community as it prioritizes hands-on experience over theoretical knowledge, reinforcing the importance of direct engagement with model APIs. The curriculum includes real-world case studies and practical exercises, promoting best practices such as the "measure before you tune" philosophy. With evaluations as a central theme, students will cultivate skills critical for deploying reliable and effective AI solutions. Besides being OpenAI-compatible, the notebooks encourage critical thinking about when to utilize frameworks versus building from scratch, ultimately preparing engineers for roles that demand agility and deep technical expertise in AI applications.