ChatGPT and Large Language Models: How They Really Work
LLMs still predict the next token, but the key improvements of recent years were built on top of that mechanism. Models learned to say "I don't know," call external tools, check their own steps and spend more compute on reasoning. That is why ChatGPT and similar systems have become noticeably more useful: they more often rely on real data, not just on memory from training.
AI-processed from Habr AI; edited by Hamidun News
ChatGPT and other large language models still build their answers by predicting the next token, but this deceptively simple mechanism proved far more productive than even many researchers expected.
How do large language models like ChatGPT actually work?
They build an answer by predicting the next token, one step at a time. The sharp rise in LLM quality is not explained by scale alone, but by how self-critique, tools and multi-step reasoning were added on top of the base model.
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