• It is a survey course by design, introducing topics rather than walking through code, so Hamel Husain tells students to apply each part to a personal project or the material won't stick.
• Do the instruction-tuning tutorial even if you never intend to fine-tune: it forces you through working with open-weight models, generating synthetic data, managing prompts, and generating predictions.
• The paid Maven cohort's recordings were re-cut for self-service, with chapter summaries, notes, and slides per talk grouped by subject, so you navigate straight to what you need instead of watching in order.

• RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
• fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
• distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
• open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.

A free, well-organized 40+ hour course distilled from a popular paid program, with annotated talks and notes from practitioners across evals, RAG, and fine-tuning — a fast way to level up on shipping real LLM products rather than toy demos.

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