42 minutesPrompt Engineering Tutorial – Master ChatGPT and LLM Responses
See how examples change model behavior and learn when few-shot prompting is worth the extra setup.
Andrej Karpathy. Karpathy spends explicit chapters on "Be aware of the model you're using, pricing tiers" and "Thinking models and when to use them," then keeps switching between ChatGPT, Claude, Gemini, Grok, and Perplexity throughout the rest of the walkthrough. It is the closest thing to watching the article's cheat sheet applied live by someone with strong opinions about when each tier earns its keep.
The model-picker habits are valuable; exact model names, prices, context limits and rankings are not stable. Re-run the comparison on your own tasks before standardizing a team recommendation.
Learn to route work across fast, cheap, deep-reasoning and source-grounded tools instead of using one model for everything.
Comfort using at least one mainstream chatbot and comparing outputs on the same task.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
42 minutesSee how examples change model behavior and learn when few-shot prompting is worth the extra setup.
36 minutesConfigure personalization deliberately instead of letting memory, instructions and project context blur together.
13 minutesDesign a meeting workflow that captures transcripts, summaries and action items without losing human review.
Hand-picked external courses that go deeper on this topic.
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."
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.
Jerry Liu
Naive RAG (vector search → stuff into prompt) is the version that ships first and disappoints fastest. This short course shows you the upgrade: an agent that plans retrieval, picks tools, compares sources, and answers multi-document questions. Two hours, immediately applicable to a real product.