AI Systems
Large Language Model (LLM): The Engine Behind Modern AI Tools
A large language model (LLM) is a type of machine learning system trained on vast quantities of text data to understand and generate human language, capable of answering questions, summarising information, writing content, and conducting conversation with a high degree of fluency and contextual relevance. LLMs are the underlying technology in tools like ChatGPT, Gemini, Claude, and Perplexity, and in AI-powered search features including Google AI Overviews.
Why large language model matters for UK businesses
LLMs are directly relevant to business visibility because they are what decides what information appears in AI-generated search responses. When a potential customer asks ChatGPT 'who is a good roofer in Stoke-on-Trent?' or asks Gemini 'what is the best approach to local SEO for a UK trades business?', the answer is generated by an LLM drawing on its training data and, in some cases, real-time web retrieval. The business that appears in that answer is the one whose entity data, content, and citations meet the criteria the LLM has learned to associate with a relevant, authoritative response.
Understanding LLMs also clarifies the limits of AI tools businesses use internally. An LLM does not know about events after its training cutoff. It can generate plausible-sounding but incorrect information (hallucinations) with the same apparent confidence as accurate information. It reflects the biases and limitations of its training data. These characteristics are relevant to how AI-generated content is reviewed before use and how much weight is placed on AI outputs versus verified information.
How Khamare Clarke applies large language model
LLMs are the underlying technology in the AI agent and AI receptionist systems built for clients. The choice of model for a given deployment depends on the task: conversational fluency, reasoning quality, latency, cost per token, and the suitability of the model's training for the domain. Not all LLMs perform equally well for all tasks, and the model choice is part of the technical specification for each AI systems build.
On the AI search side, LLMs are the systems that generate the responses in ChatGPT, Gemini, and Perplexity. The strategy for appearing in those responses is built around what LLMs have learned to value: clear, authoritative, well-structured content with strong entity signals and consistent external references. This is not the same as traditional Google SEO, even though the technical foundations overlap.
What LLMs are in common use in 2025?
The major LLMs in widespread use include GPT-4o and GPT-4 (OpenAI, underpins ChatGPT), Gemini 1.5 and Gemini 2 (Google DeepMind, underpins Google AI Overviews and Gemini chat), Claude 3 and Claude 3.5 (Anthropic), and Llama 3 (Meta, open-source). Perplexity uses a combination of models depending on the query. Each model has different strengths, training data characteristics, and access policies. The landscape evolves rapidly, with new model versions releasing on a scale of months rather than years.
Can an LLM be trained on my business's data?
There are two approaches: fine-tuning (additional training on your data to adjust the model's behaviour) and retrieval-augmented generation (RAG), which keeps the base model unchanged but allows it to retrieve relevant information from your data at the time of generating a response. For most business applications, RAG is more practical than fine-tuning: it is faster to implement, easier to update, and does not require the volume of training data that fine-tuning needs. An AI agent that answers questions about a specific business's services is typically built using RAG rather than a custom-trained model.
How do LLMs decide what to include in AI search responses?
LLMs used in AI search (ChatGPT with web browsing, Gemini, Perplexity) generate responses based on a combination of their training data and real-time web retrieval. The factors that influence citation decisions include: how authoritative the source appears (based on entity signals, backlink profile, and content quality), how directly the content addresses the query, how recently the content was published or updated, and how consistently the entity is represented across the web. This is why entity SEO, structured data, and content depth are the primary levers for AI search visibility.
Apply Large Language Model (LLM) to your business
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