Large Language Model (LLM)
An AI model trained on vast amounts of text to understand and generate human-like language, forming the engine behind tools like ChatGPT, Gemini, and Claude.
A large language model (LLM) is an AI system trained on large volumes of text to predict and generate language. LLMs power conversational assistants and answer engines, generating responses by drawing on patterns learned during training and, increasingly, on information retrieved at query time.
Training data vs. retrieval
An LLM’s base knowledge comes from its training data, which has a fixed cutoff date. To answer questions about recent or specific information, many systems combine the model with live retrieval (see Retrieval-Augmented Generation), pulling in current sources and citing them.
Why it matters for visibility
What an LLM “knows” about your business comes from two places: what it absorbed during training and what it retrieves at answer time. Influencing both — accurate, widely-referenced information and retrievable, well-structured content — is the core of optimizing for AI answers.
Related Terms
- Fundamentals
AI Optimization (AIO)
An umbrella term for optimizing a brand's content and data so it is accurately represented and cited across all AI-driven search and assistant surfaces.
- Fundamentals
Answer Engine Optimization
The practice of optimizing content so AI systems like ChatGPT, Perplexity, and Google AI Overview cite your brand when answering user questions.
- Fundamentals
Answer Engine
A system that responds to a query with a direct, synthesized answer rather than a ranked list of links — for example ChatGPT, Perplexity, or Google's AI Overviews.