Andrej Karpathy. This is the clearest end-to-end explanation on YouTube of what an LLM actually is — pretraining, tokenization, SFT, RLHF, reasoning RL, tool use, hallucinations — at the level of detail an engineer needs to reason about model trade-offs. Watch it once and the "GPT-class vs. open-weights vs. reasoning model" decisions in the article stop feeling like brand choices and start feeling like training-recipe choices.
Model names, pricing and capabilities change quickly. Use this for the decision pattern, then verify current model behavior before adopting it.
Understand the modern LLM stack well enough to reason about tokens, training, tools and failures.
Engineering background helps; no ML prerequisites, but budget for the three-and-a-half-hour runtime.
Last reviewed: May 18, 2026
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Hand-picked external courses that go deeper on this topic.
Coursera · DeepLearning.AIAndrew Ng
Real time inside an LLM, learning to prompt deliberately and recognise where generative AI is genuinely useful versus where it's a trap. Calm, no-hype teaching — the perfect bridge from "I've tried ChatGPT once" to "I use it every day with confidence."
Coursera · DeepLearning.AI + AWSAntje Barth · Shelbee Eigenbrode · Mike Chambers · Chris Fregly
When practitioners ask "what should I take if I'm serious about building with LLMs?", this is the answer. Mathematically honest without being a research paper; AWS-flavoured deployment chapters stay useful even if you'll never touch SageMaker.
Coursera · Google CloudGoogle Cloud
Google's own answer to 'what is generative AI, actually' — the non-technical counterpart to Elements of AI, aimed explicitly at sales, HR, marketing, and operations roles rather than engineers. Forty-five minutes to a real mental model of how generative AI differs from classic machine learning, with no code and no jargon.
