LLM is a large-scale language model: neural network trained on massive text sets (books, web, code) that learns to predict the next word given context. From that capability emerge more sophisticated behaviours: answering questions, summarising, reasoning, translating, writing code.
The ones that matter for SEO
In 2026 the LLMs with relevant usage share are:
- GPT (OpenAI): powers ChatGPT, ChatGPT Search, APIs.
- Claude (Anthropic): powers Claude.ai and a growing sector of B2B apps.
- Gemini (Google): powers AI Overviews and the Gemini app.
- Llama (Meta): open-source model powering third-party apps.
- Perplexity: uses a blend of models but its own web index.
How an LLM uses web content
Two phases:
- Training: absorbed content up to a cutoff date. That content gives “latent knowledge”. Your brand may appear if it was in the training set.
- Retrieval-augmented inference: for real-time queries, the LLM (or its interface) performs a web search, reads 3-10 sources and generates an answer combining that input with latent knowledge.
Implications for SEO/GEO
Two fronts to become visible in LLM answers:
- Latent knowledge: proprietary content well-positioned for years, mentions in known outlets (Wikipedia, press), clear authorship.
- Real-time retrieval: fresh content, well structured, with schema, accessible to LLM crawlers (GPTBot, ClaudeBot).
This second lane is where SEOCOM invests most in 2026, because it moves measurable short-term traffic.