What Is a Large Language Model?
A large language model is a type of neural network trained on massive datasets of text from books, websites, code, and other written material. During training the model learns statistical relationships between words, phrases, and concepts, enabling it to predict and generate coherent text in response to a prompt. The "large" in LLM refers to the scale of both the training data and the model parameters, which in modern systems number in the billions or hundreds of billions.
Well-known large language models include OpenAI's GPT-4 (which powers ChatGPT), Google's Gemini (which powers Google AI Overviews and Bard), Anthropic's Claude, and Meta's Llama. Each is trained on different datasets and optimised for different tasks, but they share the same fundamental architecture: a transformer-based neural network that processes text as sequences of tokens.
For digital marketing, the relevance of LLMs is that they are the AI layer inside generative search products. When a user asks Google a question and receives an AI Overview, a large language model read a set of candidate webpages retrieved by Google's index and wrote the composed answer they see. The LLM decides which facts to include, which source to attribute them to, and how to phrase the response. This makes LLM behaviour a critical variable for any brand pursuing generative engine optimisation.
A second marketing implication is that LLMs are probabilistic. They generate answers based on patterns in their training data and in the content retrieved at query time. If a South African business is not well represented in either its training data or the indexed content an LLM retrieves, the model may produce inaccurate or incomplete information about that business, a phenomenon called hallucination. Publishing consistent, verifiable, publicly indexed content about your brand is the most reliable way to influence what LLMs say about you.
Large Language Model In Practice
A Durban property developer asks their digital marketing team why ChatGPT describes their flagship development incorrectly when users ask about it. The AI states the wrong number of units and attributes an amenity to the development that does not exist. The developer wants to know how to correct this.
The team diagnoses the problem: the developer's website is thin on factual specifics. There is no structured data, no press releases with verified figures, and no third-party coverage that confirms key details. The LLM is filling gaps with plausible-sounding information generated from patterns rather than facts, because no authoritative source clearly states the correct information in an indexed, citable format.
The fix involves three steps. First, the team publishes a dedicated project page with specific, verifiable figures: exact unit count, confirmed amenities, registration number, municipality approval reference, and completion date. Second, they submit a press release to South African property media so that third-party sources corroborate the facts. Third, they add DefinedTerm and Product schema markup so that AI systems can identify the page as an authoritative source about this specific development. After several weeks, ChatGPT with web search and Perplexity both begin returning accurate information, citing the developer's own page and the press coverage as sources.
How large language models work
A large language model (LLM) is an AI system trained on vast amounts of text to predict likely sequences of words, which lets it generate fluent, relevant responses. It does not look up facts the way a database does; it produces language based on patterns learned in training, which is why it can be articulate yet occasionally wrong, a limitation known as hallucination. Models such as those behind ChatGPT, Claude and Gemini power the AI assistants and search features reshaping how people find information. Understanding that an LLM predicts rather than retrieves explains both its fluency and why grounding it in real, retrieved sources, as AI search does, matters.
What LLMs mean for search visibility
LLMs change how some people find businesses: instead of scanning a results page, they ask an assistant and receive a synthesised answer. Visibility then depends on whether the model, or the retrieval step feeding it, draws on your content and attributes it correctly. Two things help. First, being present and consistent across the web, so a model has accurate information to represent you. Second, publishing clear, specific, quotable content that a retrieval-augmented system can pull in and cite. Google has confirmed no special file or schema is required for its AI features; the work is being accurate, consistent and genuinely useful, which is the same foundation as classic search.
FAQ
Why does LLM training data matter for South African businesses?
LLMs are trained on text data that may underrepresent South African businesses, markets, and terminology. A business that publishes little public content may not appear in an LLM's training data and may be unknown to AI systems. Publishing consistent, factual, publicly indexed content about your brand, services, and expertise helps ensure you are represented accurately in LLM training datasets over time.
What is the difference between an LLM and a search engine?
A traditional search engine indexes webpages and ranks them in response to a query. A large language model is a neural network trained to understand and generate text. Generative search products like Google AI Overviews combine both: they use a search index to retrieve candidate pages and then use an LLM to synthesise a composed answer from those pages. The LLM does not browse the web in real time unless it is connected to a retrieval system.
Why do large language models sometimes get facts wrong?
Because they predict likely language rather than looking facts up, they can produce fluent but incorrect statements, called hallucinations. Grounding a model in retrieved, real sources reduces this, which is why web-connected AI search is more reliable than a model answering from memory alone.