Large Language Model (LLM)
A Large Language Model is a neural network trained on enormous amounts of text to predict and generate language. Models like GPT, Claude, Gemini and Llama power the chatbots and answer engines people now use for search. For SEO, the LLM matters as the reader: it ingests your content, understands meaning (not just keywords), and decides - via retrieval systems - whether you're the source worth citing. It doesn't read like a crawler; it reads like a very fast, very literal expert.
Why It Matters
For two decades, SEO meant optimising for one algorithm: Google's. Now there are dozens - each LLM-powered system is a different reader with slightly different preferences, and they're all reading your content. The good news: what makes content rank on Google - clarity, depth, structure, authority - is largely what makes an LLM cite it. The systems converge on quality.
The bigger shift is intent. LLMs answer questions conversationally, which means the queries people type change, and the content that wins answers differently: direct, quotable, grounded in evidence. Understanding how the models consume and synthesise content is now part of the SEO skill set, not a separate one.
In Practice
Write for the model's reader the way you'd write for a journalist: clear claims, named sources, structure that survives paraphrase. Keep your content accessible to AI crawlers (GPTBot, ClaudeBot, PerplexityBot). Don't stuff keywords - the models understand meaning and reward it. Monitor how models describe your brand and your industry, and correct the record when they get it wrong by publishing the definitive answer yourself.
Run your own models for the parts that matter - rankings, research, drafting. That's what the tooling behind a modern SEO operation should look like: the judgment stays human, the grunt work doesn't.
Common Mistakes
Treating LLMs as a search engine to game, or a threat to avoid. They're readers with preferences. Write content a smart reader would cite, keep it crawlable, and let the systems do the work of deciding you're the source.
Sources & Further Reading
Where to verify this yourself - Google's own documentation, industry reporting, and how we apply it at Underdog. Don't take our word for it; check the source.
Related Terms
Glossary
AI Search (Answer Engines)
Perplexity, ChatGPT Search, Gemini - the AI engines people now ask questions to instead of Google. Different rules, same goal: being the cited answer.
Glossary
RAG (Retrieval-Augmented Generation)
The technique that lets AI answers draw from your content instead of guessing. RAG is why having crawlable, citable pages still matters in the AI era.
Glossary
AI Crawlers
GPTBot, ClaudeBot, PerplexityBot - the bots that read your content for AI answers. Blocking them means vanishing from AI search.
Glossary
Semantic Search
Search engines matching meaning, not just keywords. The shift that killed keyword stuffing and made understanding intent the whole game.
Glossary
ChatGPT
OpenAI's chatbot - the product that made AI search a habit. For SEO, it's now a search engine with citation preferences.
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