Large Language Model(LLM)

A large language model (LLM) is the type of neural network behind every modern AI assistant. Claude, GPT, Gemini and Llama are all LLMs. The “large” refers to the number of parameters, billions or trillions of internal weights, and the size of the text dataset used to train them.

Practically, an LLM is a system that takes text in and produces text out. Everything else it appears to do, answer questions, write code, summarise documents, follow instructions, is built on that single capability.

How they work, roughly

LLMs are trained to predict the next word in a sequence. Given “The cat sat on the…”, the model learns that “mat” is more likely than “tractor”. Repeat this trillions of times over a huge corpus of text (books, websites, code, conversations) and the network learns deep statistical patterns about language, meaning, reasoning, and knowledge.

After base training, modern LLMs are further refined with techniques like reinforcement learning from human feedback (RLHF) and constitutional AI training, which shapes them to be helpful, harmless and honest.

What this means for Australian small business

When you use Claude Code or any other AI product, you’re querying an LLM. Costs depend on how many tokens of input/output you use. Quality depends on which model tier you pick (Opus vs Sonnet vs Haiku for Claude; GPT-4o vs GPT-4o mini for OpenAI).

LLMs are not databases, they don’t always remember facts perfectly and they don’t reliably know what’s true. For factual work (looking up an ATO ruling, checking a price), pair the LLM with search or a structured data source. For language work (drafting, summarising, rewriting), they’re remarkable.

Related terms

• Token
• Context window
• Prompt
• Agent