A large language model – LLM, for short – is a type of machine learning model that trains generative search engines on a large dataset, enabling them to generate human-like responses. ChatGPT, Perplexity.ai, Bing Chat, and more artificial intelligence tools use LLMs for Natural Language Processing (NLP). This allows them to generate more natural-sounding and conversational text responses to user queries.
For the future of GEO, understanding LLMs and how they work is crucial to understanding how AI formulates responses – and, how they might use your site in their generated answers to user queries.
How Do LLMs Work?
Large language models are deep learning models trained on large amounts of data. These models are capable of self-learning, using the dataset to process information and understand natural human language. To create a LLM, developers need to have access to large data sets first that they can use to train. During the training, LLMS learns to predict the probability of the next word in a sentence based on context. It does this through the use of a probability score, in which tokens – or words/sequences of words – are scored to determine the likelihood of a certain text response. This helps with tasks like generating text, translation, and answering user queries.
LLMs in Conversational AI
All the major conversational AI tools used today use LLMs. These models enhance the interaction between users and generative search engines by providing a more conversational response. Generative AI outputs to user queries are formulated in plain text or natural language that creates a more engaging experience to users. Its ability to analyze and interpret words or sequences of words allows it to better match its responses to the language used by the user. This makes it easier for users to get the response they need efficiently and receive a more relevant response to what they are looking for.
Why LLMs Matter for GEO
GEO, also known as generative engine optimization, requires an understanding of how generative AIs respond in a way that sounds natural to humans. Unlike SEO, GEO focuses on a more natural, humanlike response through factors like longtail keywords and concise answers.
In generative search engines, like ChatGPT and Perplexity, users do not receive a list of links to different web sources, like they would in Google or Bing. Instead, users receive a conversational answer, where several sources for the generated answer may be provided.
For example, if someone asks “How do I set up Google Ads?” ChatGPT may respond with a step-by-step guide on setting up Google Ads. In Google, users would instead receive links to various websites, in which a page on that site helps users learn how to set up Google Ads. In a way, generative AI removes a middle step in users getting to their answer.
To optimize for GEO requires a slightly different strategy than for SEO. Many LLMs use different websites’ content in their large dataset that they use to understand how to generate responses. Focusing on appearing in generative AI search results requires ensuring that AI search engines can clearly read and understand your site’s content. In addition, the goal of LLMs is to formulate a concise response that most accurately answers a user’s query. So, the information you have on your own site for a given topic should also focus on being clear, concise, and most helpful to answering user queries.
Limitations
LLMs have some limitations stemming from their training data. Depending on their training data, they may be limited in their understanding of certain topics. This leads to a lack of knowledge or expertise in certain areas. When formulating a response, the response may lack accuracy or be misleading to users.
In other words, LLMs are only as reliable as the set of data used to train them. False information in their dataset will lead to a generated response with incorrect information.
The Future for LLMs
In the last few years, LLMs have come far. ChatGPT launched in November 2022. Perplexity AI was released in December 2022. In 2025, ChatGPT is predicted to reach 1% share of the search market. The future for LLMs is only going to get more advanced.
While the future is unclear, there are a few factors that are evolving:
- Human – computer interactions will change. LLMs introduce conversational responses to what humans need. This brings a whole new interaction of how users use computers and search for everything from data analysis to content generation.
- Search will change. Instead of navigating through several websites to find an answer, users can now get a straightforward response from an AI chatbot. Even in Google’s organic search, there has been the introduction of AI overviews.
- LLM Functioning will improve. The biggest limitation to LLMs is the accuracy and knowledgebase of its dataset. The more LLMs are tested for functionality, the more they will improve over time.
With any advancements to digital marketing, it’s best to stay ahead of the curve. By understanding the complexities of generative search engines and how they work, business owners can ensure that their website continues to appear in search, no matter what tools users continue to use.
