Large language models (LLMs) are a type of artificial intelligence system trained on vast quantities of text data to understand, generate, and reason with human language. This training data is drawn from sources such as websites, books, academic papers, and code repositories.
They are called “large” models because of the enormous scale at which they are trained, both in terms of the volume of data ingested and the number of parameters used to build them.
LLMs work by learning statistical patterns in language during training, which allows them to predict and generate coherent, contextually appropriate text in response to a given input — known as a prompt. Rather than retrieving pre-written answers from a database, they construct responses word by word based on what the model has learned is most likely to be relevant and accurate. Well-known examples of large language models include Gemini, OpenAI’s GPT-4 (which powers ChatGPT), Meta’s Llama, and Anthropic’s Claude.
How LLMs work (at a high level):
During training, the model is exposed to enormous volumes of text and learns to recognise patterns, relationships between concepts, and the structure of language.
During inference, which is when the model is actually being used, it takes a prompt as input and generates a response based on the patterns and knowledge encoded during training.
Many LLMs are further refined through a process called Reinforcement Learning from Human Feedback (RLHF), where human reviewers rate the quality of outputs, and the model is adjusted to produce more helpful, accurate, and appropriate responses.
Common Applications of LLMs
Here are some common applications of LLMs that are relevant to SEO and search:
- Powering AI-generated search features such as AI Overviews and AI Mode.
- Driving conversational AI tools such as ChatGPT, Gemini, and Microsoft Copilot that users are increasingly turning to as an alternative to traditional search.
- Assisting with content creation, keyword research, and data analysis within SEO workflows.
- Enabling semantic search capabilities that allow search engines to better understand the meaning and intent behind queries, rather than relying purely on keyword matching.
- Powering retrieval-augmented generation (RAG) systems that combine LLM capabilities with real-time web retrieval to provide up-to-date responses.
Large language models are fundamentally reshaping the search landscape, and understanding how they work is becoming an increasingly important part of SEO.
On one hand, LLMs are changing how users find information. A growing proportion of people are turning to AI-powered tools rather than traditional search engines for answers, particularly for informational queries.
On the other, LLMs are being integrated into search engines themselves, powering features such as AI Overviews, and AI Mode that generate direct answers rather than simply listing links, all of which has significant implications for click-through rates and organic traffic.
For SEO professionals, LLMs raise important questions about how AI systems discover, evaluate, and surface content. Content that is clear, well-structured, factually accurate, and supported by strong authority signals is better positioned to be retrieved and cited by LLM-powered search features. Understanding concepts such as chunking, embeddings, hallucinations, and retrieval-augmented generation provides SEO professionals with a more informed foundation for adapting their strategies to a search environment increasingly shaped by large language models.
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