An AI model trained on vast text to predict and generate language, powering tools like ChatGPT.
A large language model is a type of AI model trained on very large amounts of text to predict the next piece of text given what came before. From that simple objective, at scale, models learn to answer questions, summarise, translate, write code, and hold a conversation. LLMs power tools such as ChatGPT, Claude, Gemini, and the AI features increasingly built into search.
An LLM does not look facts up or reason like a person. It generates plausible text based on patterns learned during training, which is why it can be fluent and useful but also confidently wrong.
The model breaks text into tokens and learns statistical relationships between them across billions of examples. At use time it predicts the most likely next tokens one at a time, guided by your prompt. Its knowledge is frozen at training time unless it is given extra information at runtime.
LLMs now shape how people find information: AI assistants and AI-powered search summarise answers directly and cite a handful of sources. That changes the game for visibility and for building products.
The main pitfall is trusting an LLM as a database of facts. Because it generates rather than retrieves, it can hallucinate, invent citations, or state outdated information confidently. Serious applications ground the model in verified data rather than relying on its memory.
Grounding an LLM's answers in retrieved documents so it responds from real, current data.
Optimising content to be cited and surfaced by AI answer engines, not just ranked in blue links.
A proposed plain-text file that gives AI models a curated, easy-to-read map of your site's content.
Common questions
Straight answers on how this fits your marketing and build.
They do not understand in the human sense. They predict likely text from patterns learned in training. The output can be remarkably useful, but it is generated, not looked up or reasoned from first principles.
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