LLM (Large Language Model)

A large language model (LLM) is an AI system trained on enormous amounts of text to predict and generate human-like language. Models such as GPT, Gemini, and Claude power the chatbots, AI Overviews, and answer engines that now sit alongside — and increasingly in front of — traditional search results.

How an LLM actually produces an answer

At its core, an LLM predicts the next most likely word (technically, token) given everything before it, one piece at a time. During training it absorbs patterns from billions of sentences; at the moment you ask a question, it isn't looking anything up in a database — it's generating a statistically plausible response from those learned patterns. That distinction explains a lot: it's why an LLM can write fluently on nearly any topic, and also why it can state something false with complete confidence, a failure known as a hallucination.

Why LLMs changed the SEO playbook

When a user asks ChatGPT "what's the best CRM for a small law firm?" and it names three tools with a short rationale, no blue links appear — yet brands were still recommended. Earning a place in that answer is a new discipline, one that overlaps with generative engine optimization. It rewards content that states facts clearly, earns third-party citations, and is easy for a model to parse and attribute.

• Structured, well-sourced content is easier for models to cite accurately.
• Consistent brand facts across the web reduce the chance a model gets you wrong.
• Some models retrieve live pages (via RAG) before answering, so being crawlable and clear still matters.

FAQ

Do LLMs know current information?

Only up to their training cutoff, unless they're connected to live retrieval or web browsing. A standalone model can't know yesterday's news on its own.

Optimizing for these systems is the focus of SEO for ChatGPT, and it builds on the same foundations as machine learning.