4 minutesUsing AI Wisely for School Success
State one rule of thumb for telling AI homework help apart from AI cheating.
Andrej Karpathy. A founding OpenAI engineer giving the cleanest hour-long explanation of what an LLM actually is, in plain English. The "psychology of an LLM" section is exactly the mental model the article is trying to install — why these models hallucinate, why they're good at vibes and bad at counting, why prompting is closer to coaching than coding.
This remains one of the strongest conceptual explanations. Avoid copying anthropomorphic language too literally: models do not think or know; they generate likely continuations under constraints.
Build a non-mystical mental model of LLMs as prediction systems with context windows, training data, sampling and failure modes.
None; the talk is long but beginner-friendly.
Last reviewed: May 18, 2026
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Andrew 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."
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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.
Google 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.