Building OpenAI o1
OpenAI. Three minutes with the o1 team describing the moment the model started questioning its own reasoning during RL training. Useful as the primary source for the article's claim that the chain-of-thought is now happening inside the model, not in your prompt.
AI Expert note
Keep this as historical source material, not as current implementation guidance. It explains why the category emerged, but production choices should be based on the current model lineup and API docs.
What you should get from this
See the original product/research framing that made reasoning models different from ordinary chat models.
Watch or know first
Watch the primary pick first or read the companion article's reasoning-model section.
Watch next
Continue through the same learning path with the next curated companion videos.
92 minutesn8n Masterclass: Build AI Agents & Automate Workflows (Beginner to Pro)
Extend a simple agent into multi-step workflows with memory, error handling and realistic business integrations.
Watch next
31 minutesHow To Build An AI Customer Support Agent with n8n (free template)
A practical n8n workflow that plugs into Zendesk, Gorgias or Freshdesk, replies to tickets with a RAG-backed answer, and feeds solved tickets back into the knowledge base.
Watch next
25 minutesOWASP's Top 10 Ways to Attack LLMs: AI Vulnerabilities Exposed
Use the OWASP LLM risk categories to review tool access, output handling and sensitive-data exposure.
Watch nextRelated videos
Take it further
Hand-picked external courses that go deeper on this topic.
DeepLearning.AIChatGPT Prompt Engineering for Developers
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."
Coursera · Vanderbilt UniversityPrompt Engineering for ChatGPT
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.