Large Language Model (LLM)

A large language model (LLM) is a deep learning model, usually based on the transformer architecture, trained on huge text collections to predict the next token in a sequence. Scale, in both training data and parameters, lets the same model handle many language tasks without task-specific training: writing, summarising, translating, extracting entities, classifying and answering questions. LLMs power assistants such as ChatGPT, Claude and Gemini and the generative layers of search, such as AI Overviews and AI Mode.

How it works

• Tokens: text is split into tokens (words or word pieces), each represented as an embedding.
• Pre-training: the model learns to predict the next token across a vast corpus, which teaches grammar, facts and patterns.
• Post-training: instruction tuning and feedback from people make it follow instructions and respond helpfully.
• Inference: given a prompt, it generates one token at a time, choosing likely continuations. The context window limits how much text it can consider at once.

LLMs do not look facts up by default; they generate text from patterns learned in training. Search products therefore pair them with retrieval: they fetch documents first, then generate an answer grounded in them and cite sources.

Encoder models like BERT use the same transformer building blocks but are built to understand text and produce embeddings, while generative LLMs such as GPT models are built to produce text.

How it’s used in SEO and marketing

LLMs are both a tool and a channel.

• As a tool: classifying keywords and SERP results, naming clusters, drafting meta descriptions and briefs, extracting entities, and running agent workflows.
• As a channel: AI search systems decide which sources to retrieve and cite, which is why clear entities, structured data and specific, extractable passages matter.
• As a research subject: tracking how assistants describe your brand and which sites they co-cite with you.

Example

In MLforSEO’s guide to automating SERP analysis, an LLM with structured outputs classifies results, intent and SERP features, clusters People Also Ask questions and powers an agent. Running every AI step live cost about 17 cents of API credit in total. The guide also records where the model went wrong, which is why a human keeps the final say.

Limitations and things to watch

• They can state false information fluently (hallucination), so ground them in your data and verify outputs.
• The same prompt can return different answers; MLforSEO’s entity benchmark saw this with a re-run.
• Knowledge has a cut-off unless the model retrieves live sources.
• For narrow, repeatable tasks, a purpose-built API or small model can be cheaper and more consistent.

Learn it on MLforSEO

• Entity-Based Search in the LLM Era
• How LLMs Co-Cite: Building Authority by Association
• How to Automatically Generate Meta Descriptions with Python: From BERT to LLMs
• 10 Ways to Automate SERP Analysis with AI
• AI Search Optimization & Agentic SEO course