ChatGPT looks like magic from the outside, but the recipe is well-documented if you know where to look, and this three-minute explainer condenses the core ideas. The system starts from a GPT-3-class base model, then applies the InstructGPT pipeline: supervised fine-tuning on human-written demonstrations, a reward model trained from human preference rankings, and reinforcement learning from human feedback to align the model with what users actually want.

The striking result behind the product is that a 1.3B InstructGPT model beat the 175B GPT-3 in human preference evaluations, with less toxic output and better instruction following, all with a hundred times fewer parameters. For anyone building conversational voice agents, alignment-tuned speech LLMs, or trying to reason about why ChatGPT feels different from raw completion APIs, this compressed overview covers the mechanism without the paper-reading tax. Hit play if you want the mental model in the time it takes to make a coffee.