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What an LLM is
An LLM (Large Language Model) is an artificial intelligence model trained on billions of parameters from massive amounts of text data. It can understand natural language, generate text, answer questions, and perform complex tasks like document synthesis, translation, and code generation.
LLMs run on a Transformer architecture, introduced by Google in 2017. This architecture lets them evaluate the relative importance of each word in a sentence, creating contextual understanding that previous models couldn't achieve.
Key LLMs in 2026:
- GPT-4o / GPT-5 (OpenAI): powers ChatGPT
- Gemini 2.5 (Google): integrated into Google Search and AI Overviews
- Claude (Anthropic): focused on safety and long reasoning
- Llama 4 (Meta): open-source, up to 10 million tokens of context
- Mistral (Mistral AI): competitive on performance/cost ratio
What's changing for LLMs in 2026
The LLM ecosystem has evolved dramatically. The global market is estimated at over $10 billion, and 67% of organizations have already adopted LLMs in their operations.
Three structural trends:
- Agentic capabilities: LLMs no longer just generate text. They plan and execute tasks autonomously, interacting with tools and APIs via protocols like MCP
- Extended context windows: Llama 4 Scout reaches 10 million tokens. This evolution reduces reliance on classical RAG for document queries
- Multimodality: models now process text, images, audio, and video simultaneously
The gap between open-source and proprietary models is narrowing. It was about a year in 2024, and dropped to six months in 2025. Open-weight models are increasingly viable for sovereign and private deployments.
Why LLMs are at the core of GEO
For any AI visibility strategy, understanding LLMs is not optional. They're the ones that decide which sources to cite in their responses. And each LLM has its own preferences.
What we see at Vydera: citation algorithms vary significantly across LLMs. ChatGPT relies heavily on Bing and its training data. Perplexity favors fresh, well-structured sources. Claude gives less weight to web search and more to its knowledge base. Google AI Overviews draws from its own index.
Optimizing for a single LLM isn't enough. An effective GEO strategy accounts for the specifics of each model.
How LLMs select their sources
LLMs with web search (ChatGPT Search, Perplexity, AI Overviews) use a multi-step pipeline:
1. Query decomposition. The model breaks the question into parallel sub-queries (query fan-out).
2. Source retrieval. A RAG system retrieves the most relevant content from a web index or vector database.
3. Selection and synthesis. The LLM evaluates relevance, credibility, and freshness, then synthesizes its response citing the sources it deems most reliable.
Your content must survive each of these steps to get cited.
Sources and references
- Sebastian Raschka, The State of LLMs 2025: Progress and Predictions
- Aggarwal et al., GEO: Generative Engine Optimization, ACM SIGKDD 2024
- Hostinger, LLM Statistics 2026: Adoption, Trends, and Market Insights
Go further
Understanding LLMs means understanding the rules of AI visibility. At Vydera, we analyze citation behavior across models to adapt our clients' content strategy. See our case studies or explore the Vydera Lab.
