The basicsAI glossary
LLM
"Large Language Model": an AI model trained on vast amounts of text to understand and generate language. ChatGPT, Claude, and Gemini are LLMs.
In other words
A reader who would have read almost the entire internet and would finish your sentences better than you.
Alyriaz articles that mention it
- What is a "foundation model"? The building block on which everything else is built
The term "foundation model" refers to a general-purpose model trained at very large scale, then adapted to many tasks. We explain this concept and why it underpins the entire sector's economy.
- What is a "system prompt"? The invisible instructions that shape every response
Every AI assistant receives, before your message, an invisible set of instructions that defines its role and limits. We explain what a system prompt is, what it contains, and why it accounts for many surprising behaviours.
- What is an AI's "temperature"? The setting that decides whether it's sensible or inventive
Temperature is the setting that controls the degree of randomness in an AI's responses. We explain how it works, why a low temperature doesn't guarantee truth, and how to choose it based on your use case.
- Why would an AI capable of solving a mathematical conjecture get two numbers wrong when adding them?
AI models excel at advanced mathematics yet sometimes fail at basic arithmetic. Here's why: they don't calculate—they predict text, and numbers are tokenised in a way that makes arithmetic surprisingly difficult.
- What is a "reasoning model"? These AIs that take the time to think before answering
Deep Think mode, reasoning effort, sub-agents: recent models no longer respond immediately, they think first. We explain what a reasoning model is, what actually happens during that pause, why it improves answers, and why it sends the bill through the roof.
- What is an "LLM"? The engine behind ChatGPT, Claude and Gemini, explained simply
LLM, large language model: the term is at the heart of all AI news, yet rarely explained. We break down simply how this technology that predicts the next word works, why this seemingly mundane ability is enough to produce text, code, and reasoning, and where its limits lie.