liveBook · Manning

GPT model

As explained in Grokking AI Applications

GPT is a generative pretrained transformer language model that predicts likely continuations of text.

Think of GPT as an intelligent autocomplete: it generates text by predicting the most likely next word, producing coherent continuations rather than following hand-written rules. section 1.5

Understand GPT within the larger learning process:

  • GPT begins as an unsupervised model that learns language patterns from data and completes sentences. section 1.5
  • Fine-tuning then uses supervised and reinforcement learning to improve the initial output for human conversation. section 1.5

From completion to conversation

Given the beginning of a sentence, GPT selects a likely continuation. Further human refinement helps turn this completion ability into the conversational behavior associated with ChatGPT. section 1.5

How Grokking AI Applications covers it

How deep it goes
Explains prediction, unsupervised pretraining, and later refinement, while stopping short of detailed model internals.
Examples
Uses sentence completion, ChatGPT conversations, and a three-stage learning diagram as concrete illustrations.
What it assumes
Introduces machine-learning categories and GPT's role without assuming knowledge of model internals.
What you can do after
Evaluate/understand GPT's role in language generation and distinguish pretraining from conversational refinement.
How it connects
Connects GPT with large language models, unsupervised learning, supervised learning, reinforcement learning, fine-tuning, and ChatGPT.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Applications. The section links point to the parts of the book it draws on.