1 Big Picture, What is GPT?

Artificial intelligence has surged into everyday conversation, largely due to ChatGPT, and this chapter sets the stage by explaining what GPT is and why it matters. It introduces GPT as a Generative Pre-trained Transformer, situating it within the broader category of large language models that create new content based on patterns learned from vast text data. The authors aim to demystify how these systems work in plain language, clarifying what they can and cannot do and why. The discussion emphasizes that ChatGPT’s impact stems from scale—models that are far larger and trained on far more data than earlier approaches—enabling convincing conversation, summarization, and instruction following.

At a high level, GPTs are language models from the field of natural language processing that learn statistical relationships among words, not human-like understanding. They rely on neural networks—especially Transformers—to encode and generate text, and their effectiveness arises from massive datasets, enormous parameter counts, and specialized hardware. The chapter contrasts human, interactive language learning with the model’s static training process, stressing that the learned representation is powerful but fallible and steerable. A central lesson is that “bigger often beats clever”: with LLMs, performance gains frequently come more from more data and larger models than from intricate algorithmic tweaks. Training such systems is computationally expensive, so the book focuses on durable concepts rather than fast-changing implementation details.

The chapter also frames both the promise and the risks. GPTs can accelerate tasks like summarization, content creation, and dialogue, yet they can make confident mistakes, fail at seemingly simple problems, and be manipulated into harmful outputs. Responsible use requires skepticism, validation, and thoughtful system design that anticipates failure modes and ethical concerns. The book targets a broad audience with minimal coding and math prerequisites, offering the vocabulary and mental models needed to evaluate applications, understand limitations, and decide when to use or avoid LLMs. By the end, readers should be equipped to engage with GPTs’ capabilities and constraints in real-world contexts.