An LLM (Large Language Model) is a foundational concept for GEO strategy — an AI model that has two aspects: "learned knowledge" and "real-time retrieval." It has a knowledge limitation in that it does not know about events after its training cutoff, and designs that supplement this limitation through RAG and Grounding have become widespread. GEO strategy has two directions — "improving quality as training data" and "establishing structures that are easy to retrieve and cite" — and these two directions are derived from how LLMs work. Understanding how LLMs work serves as the foundation for explaining the "why" of GEO strategy.
What You Will Learn From This Page
- The meaning, definition, and main types of LLMs
- How learning works and the limitations of knowledge
- Positioning in GEO strategy
- The relationship with RAG and Grounding
- Common misconceptions
What Is an LLM?
LLM stands for Large Language Model. It is an AI model that learns from vast amounts of text data on the internet and can generate and understand natural text that reads like something written by a human.
As the name "large" suggests, it has tens of billions to hundreds of billions of parameters (the weights of learned knowledge), trained using enormous computational resources. Major AI services such as ChatGPT, Claude, and Gemini are all built on LLMs as their foundation.
The table below summarizes major AI services and the developers of the LLMs that serve as their foundation.
Major AI Services and Their Underlying LLMs
| Service |
Developer |
Underlying LLM |
| ChatGPT |
OpenAI |
GPT series |
| Claude |
Anthropic |
Claude series |
| Gemini |
Google |
Gemini series |
| Grok |
xAI |
Grok series |
| Perplexity |
Perplexity AI |
Uses and switches between multiple foundation models |
Each service is built on a different LLM, and in GEO strategy, it is important to be aware of the differences between services.
How LLM Knowledge Works and Its Limitations
LLMs form their knowledge based on data up to the point of training. As a result, they have no knowledge of events after the training data cutoff. They also may not be able to accurately respond about information not included in training data, or brands, people, and services that are too obscure to appear much in the training data.
To supplement these limitations, RAG (a mechanism for searching external information before generating a response) and Grounding (a mechanism for grounding responses to specific sources) are being utilized.
Genview's Definition
In the context of GEO strategy, Genview defines an LLM as "the foundation of the AI that GEO strategy targets — a model with a mechanism by which content is evaluated and cited from two aspects: the formation of learned knowledge and real-time information retrieval."
This definition represents Genview's perspective and does not reflect an industry-wide consensus.
Genview's adoption of this positioning is based on three points.
- Web content collected by learning-type crawlers such as GPTBot and ClaudeBot may be utilized as training data for the next generation of LLM models. Establishing a site's expertise, consistency, and credibility may lead to a higher likelihood of being judged as high quality as LLM training data. However, this is an inference as of May 2026 and has not been officially disclosed by any of the companies involved.
- By combining with RAG, LLMs can generate responses while supplementing the limitations of learned knowledge. ChatGPT Search and Perplexity are understood to adopt this RAG-like approach, and establishing content that is "easy for LLMs to retrieve and cite" translates into GEO strategy practice.
- LLMs are influenced by how their own brand and services are described in the training data. Consistent mentions on the web, accurate definitions, and the accumulation of expertise are believed to serve as the foundation for LLMs to recognize one's company as a specific "who."
Parent Concepts and Related Terms
LLMs are positioned as the prerequisite concept for GEO strategy. The following organizes the concepts related to LLMs.
Parent Concepts
- AI (Artificial Intelligence): LLMs are a type of artificial intelligence. They are positioned as large-scale neural network models that excel particularly at natural language processing (NLP).
- GEO (Generative Engine Optimization): The overall initiative to optimize brand visibility in AI-generated responses. Understanding how LLMs work serves as the foundation for explaining the "why" of GEO strategy.
Related Terms
- RAG (Retrieval-Augmented Generation): The mechanism by which LLMs search for and retrieve external information before generating a response. A representative approach for supplementing the limitations of LLMs' learned knowledge.
- Grounding: The mechanism by which LLMs ground their responses based on specific information sources. Grounding improves LLM response accuracy and reduces hallucination.
- Parameters: The weights of knowledge an LLM acquires through training. Having tens of billions to hundreds of billions of parameters is what makes them "large-scale." From a GEO strategy perspective, web content as training data may influence parameters.
- Hallucination: The phenomenon where LLMs generate information that differs from fact as if it were accurate. RAG and Grounding are utilized as means to reduce hallucination.
- Entity: A target that AI and search engines recognize as "a concept, thing, person, or organization with distinct meaning." Whether an LLM accurately recognizes a brand as an Entity may affect the effectiveness of GEO strategy.
- Inference: The process by which a trained model receives input and generates a response. The core operation of an LLM and the place where GEO strategy results actually appear.
- Token: The minimum unit by which LLMs process text. Since LLMs divide all text into tokens before processing, this affects content information density and context window efficiency.
Common Misconceptions
The following three misconceptions about LLMs are frequently observed.
Misconception 1: "LLMs know everything."
LLMs only have information contained in their training data. They cannot accurately respond about events after the training cutoff, brands or people that appear only rarely in training data, or non-public information. From a GEO strategy perspective, understanding that "how one appears in LLM training data" influences long-term brand recognition is important.
Misconception 2: "ChatGPT and LLM are the same thing."
ChatGPT is an AI service provided by OpenAI, while LLM is the category of the model that serves as the foundation of that service. ChatGPT is a service built using an LLM called GPT, and the two have a "service" and "model category" relationship. Gemini and Claude are similarly services based on their respective different LLMs.
Misconception 3: "Optimizing for LLMs equals all of GEO strategy."
GEO strategy encompasses not only impact on LLM training data, but also retrieval in the RAG Retrieval phase, selection of grounding bases, and citation acquisition, among multiple elements. Optimization for LLMs (improving quality as training data) is an important aspect of GEO strategy, but not the whole of it.
FAQ
- Q: How does understanding LLMs help with GEO strategy?
- A: Understanding that LLMs handle information through two methods — "learned knowledge" and "real-time retrieval" — reveals that GEO strategy measures have two directions. The measure for the former is accumulating content with high expertise, consistency, and credibility. The measure for the latter is establishing structures such as BLUF, FAQ, and definition statements that are easy to retrieve and cite.
- Q: Can I check how my company is recognized by LLMs?
- A: You can check how LLMs describe your company by directly asking about your brand name or service name on services such as ChatGPT, Claude, Gemini, and Perplexity. If the responses are inaccurate, ambiguous, or outdated, it is a sign that LLM recognition improvement is needed.
- Q: How are LLMs and RAG related?
- A: In contrast to LLMs' basic operation of "responding from learned knowledge," RAG is an extension that "retrieves information from external sources before responding." Using RAG can supplement LLM knowledge limitations (cutoff and information gaps). ChatGPT Search and Perplexity are understood to utilize RAG, and in GEO strategy, it is important to be conscious of both LLMs and RAG.