How I use LLMs
Andrej Karpathy. A follow-up where Karpathy actually sits in front of ChatGPT, Claude, Gemini and Perplexity and uses them like a power user. Watch this after the article and his "Intro" — you'll see the mental model put into practice in real time, including the small choices (which model, which mode, when to paste vs upload) that separate frustrated beginners from people who get useful answers on the first try.
AI Expert note
The workflow habits are stronger than the exact product choices. Re-check current model defaults, file behavior and privacy controls before copying any upload-heavy pattern into client or workplace use.
What you should get from this
See how an experienced user switches tools, modes and context strategies while keeping verification in the loop.
Watch or know first
Watch or read a basic LLM explanation first so the live workflow does not feel like magic.
Related videos
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.