How GPT, Claude and Gemini work under the hood

The backbone of today’s leading AI chatbots—GPT, Claude, and Gemini—traces back to a 2017 Google research paper titled *Attention Is All You Need* by Ashish Vaswani and colleagues. This introduced the transformer architecture, which revolutionized natural language processing by enabling models to process entire sentences simultaneously rather than word-by-word. The key innovation is the attention mechanism: instead of sequentially reading text and losing context (like a reader forgetting the start of a sentence), transformers weigh the importance of every word in relation to others. This allows them to resolve ambiguities—like correctly linking “it” to “guitar” in a sentence—and produce coherent, context-aware responses.

Training these models involves ingesting vast datasets: books, Wikipedia, scientific papers, code repositories, and online discussions totaling trillions of words. The core task is simple but powerful: predict the next word in a sequence. Through billions of iterations, models like GPT-4 (released March 2023) develop deep linguistic intuition, enough to generalize across domains—such as scoring in the 90th percentile on the Uniform Bar Exam without targeted legal training. However, raw language modeling isn’t enough for reliability. Enter Reinforcement Learning from Human Feedback (RLHF), where human evaluators rank model outputs, guiding the system to produce responses humans prefer. This turns unpredictable autocomplete into helpful, conversational assistants—why you trust Claude to clarify, GPT to summarize, and Gemini to research.