At their core, Gemini, ChatGPT, and other such systems are powered by a type of technology known as a large language model — or LLM, for short. In the simplest possible terms, an LLM looks at massive amounts of real-world language data and then uses that to learn patterns and predict the most likely next word over and over, in response to any prompt it’s given.
In other words, these engines don’t truly understand context or “think” about the answers they’re giving you in any human-like sense. They merely predict words, based on patterns observed in sprawling sets of human-created data, and then string those words together to form sentences and, eventually, entire paragraphs and documents whenever they’re summoned and given a task.
Somehow, that’s translated into tech companies plastering them into every possible place and presenting them as the end-all answers for every possible purpose — everything from replacements for search to replacements for writing in Gmail, Google Docs, and other such places. (The situation is even more extreme in other non-Google arenas, too — like with Microsoft’s legal-document-creating disaster-waiting-to-happen.)
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