A large language model is the AI technology behind tools like ChatGPT, Claude, and Gemini. Here's what it is, how it works, and what it means for your business.
A large language model (LLM) is the AI technology behind every major AI assistant you have used. ChatGPT, Claude, Gemini — these are all applications built on top of large language models.
Understanding what an LLM is — and is not — helps you make better decisions about when and how to apply AI in your business.
An LLM is an AI system trained on text. During training, it processes billions of words of text — books, articles, code, websites, conversations — and learns the patterns within them: how language works, what concepts mean, how reasoning unfolds, what facts are commonly associated with what.
When you give it a prompt, it generates a response based on these learned patterns. It does not look things up in a database or execute rules. It generates text that is statistically likely to be a useful, accurate response to your input, drawing on everything it learned during training.
This is why LLMs are flexible. The same model that drafts a contract can summarise a report, write Python code, answer a customer question, or reason through a business problem. Language understanding is the underlying capability; all the applications are expressions of it.
Claude (Anthropic) — Available as Haiku (fast, cheap), Sonnet (balanced), and Opus (most capable). Particularly strong for long documents, precise instruction-following, and coding.
GPT-4o (OpenAI) — The flagship model behind ChatGPT. Broad knowledge, multimodal (text + images), large integration ecosystem.
Gemini (Google) — Google's LLM family. Strong for tasks requiring Google-connected data. Built into Google Workspace tools.
DeepSeek — A Chinese LLM that has gained attention for strong performance at lower cost. Increasingly used as a cost-effective option for high-volume deployments.
They do not know what is happening right now. LLMs are trained up to a knowledge cutoff date. Events after that date are unknown to them unless they are given web access or external data.
They can be wrong. LLMs generate plausible-sounding responses, but they can produce incorrect information with confidence. This is called "hallucination." For high-stakes decisions, LLM outputs should be verified.
They do not take actions by themselves. An LLM generates text. To take actions — sending an email, updating a database, calling an API — it needs to be connected to tools and an orchestration layer. This is what AI agents add on top of the base LLM.
A raw LLM is like a brain without hands. It can understand and generate language but cannot do anything in the world.
To deploy an LLM usefully in a business context, you combine it with:
The platforms and frameworks that do this — n8n, OpenClaw, LangChain, the OpenAI Agents SDK — are the layer between the raw LLM and a functioning business application.
WhatWill AI builds AI systems that put LLMs to work in Australian businesses. Book a discovery call to find out what is worth building for your situation.
A large language model (LLM) is a type of AI system trained on vast amounts of text data to understand and generate human language. It learns patterns, facts, reasoning structures, and language from its training data, and uses this to respond to prompts, answer questions, write text, generate code, and reason through problems. GPT-4o (OpenAI), Claude (Anthropic), and Gemini (Google) are all large language models.
An LLM is trained by processing enormous amounts of text and learning to predict what word or token comes next in a sequence. Through this process, it develops representations of language, concepts, and relationships. When you give it a prompt, it generates a response token by token, drawing on these learned representations. Modern LLMs use a transformer architecture and are refined with human feedback to produce helpful, accurate responses.
An LLM is the underlying model — the AI system that understands and generates language. A chatbot is an application built on top of an LLM that adds a conversation interface, memory, and sometimes tools or integrations. ChatGPT is a chatbot application; GPT-4o is the LLM that powers it. Claude.ai is a chatbot application; Claude Sonnet or Opus is the LLM underneath.
For most business applications, Claude (Anthropic) and GPT-4o (OpenAI) are the leading choices. Claude has advantages in long-document processing and precise instruction-following. GPT-4o has a broader ecosystem and integrations. Both providers also offer smaller, cheaper models (Claude Haiku, GPT-4o Mini) for high-volume, lower-stakes tasks. The best LLM is the one that performs best on your specific use case.
Businesses use LLMs to automate tasks that require language understanding: drafting documents, processing incoming emails, extracting information from contracts or forms, answering customer questions, classifying support tickets, generating reports, and building AI agents that reason and take actions. Any task that involves reading, writing, or reasoning about text is a potential LLM use case.
WhatWill AI builds and runs AI systems for Australian businesses. Book a free 30-minute discovery call — we’ll tell you exactly what’s worth building for your situation.