Large Language Models explained briefly
3Blue1Brown. The cleanest eight-minute mental model of an LLM on YouTube. Grant Sanderson walks through "next-word prediction," parameters, training, and reinforcement learning from human feedback without ever drifting into hype. Watch it right after the article and the phrase "AI is just autocomplete on steroids" stops being a slogan and starts being a useful shorthand.
What you should get from this
The cleanest eight-minute mental model of an LLM on YouTube.
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Take it further
Hand-picked external courses that go deeper on this topic.
Coursera · DeepLearning.AIGenerative AI for Everyone
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 + AWSGenerative AI with Large Language Models
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.
Anthropic AcademyMCP: Build Rich-Context AI Apps with Anthropic
MCP is the protocol that's quietly replacing one-off tool integrations across the AI tooling ecosystem. Learn it from the source. By the end you'll have built and deployed your own MCP server, connected an LLM client to it, and understood why this standard is the closest thing the field has to USB-C.