131 minutesHow I use LLMs
Learn to route work across fast, cheap, deep-reasoning and source-grounded tools instead of using one model for everything.
Andrej Karpathy. Karpathy walks through his actual day-to-day use across ChatGPT, Claude, Gemini, Grok, and Perplexity, screen-sharing live conversations rather than polished one-shots. You see when he reaches for a follow-up, when he switches models mid-thread, and when he abandons a line and starts over — exactly the iterative loop the article describes, modeled by someone who builds these systems for a living.
Don't mine it for techniques — the value is watching when he follows up, switches models or starts over, which is exactly the iterative habit the article argues for. Fine to watch in chunks.
You can recognize when to follow up, when to switch models and when to start a fresh conversation, calibrated against real day-to-day usage.
None — watchable cold; the only real barrier is the two-hour-plus runtime.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
131 minutesLearn to route work across fast, cheap, deep-reasoning and source-grounded tools instead of using one model for everything.
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.
Hand-picked external courses that go deeper on this topic.
Dr. Jules White
The academic complement to DeepLearning.AI's short course — same discipline, longer arc, written for people who don't code. Dr. White teaches prompting as a set of reusable patterns (Ask for Input, Outline Expansion, Fact Check List, Menu Actions) rather than tricks. After this you'll prompt LLMs like a designer, not a guesser.
Isa Fulford · Andrew Ng
Ninety minutes to a year's worth of intuition. If you've started writing code that calls an LLM — or you're about to — this is the most efficient course online for closing the gap between "playing with ChatGPT" and "shipping a feature that calls an LLM."
Microsoft Learn
Prompt engineering, but for the tool most office workers will actually touch first. Microsoft's own four-part prompting framework (goal, context, source, expectation) is a genuinely useful mental model, and unlike the generic ChatGPT prompting courses already in our catalog, this one is grounded entirely in Microsoft 365 Copilot's specific quirks and grounding behavior.