Glossary

Every abbreviation and piece of jargon we use, explained in plain English. House rule: no three-letter acronym appears in a transmission without being spelled out and linked here.

Agent

An AI system that can plan and carry out multi-step work on its own — browsing, writing files, calling other software — rather than answering a single question.

Artificial General IntelligenceAGI

A hypothetical AI capable of matching or exceeding human performance across virtually all cognitive work, rather than excelling at one narrow task.

Benchmark

A standardised test used to compare models — for example, solving maths problems or fixing real software bugs. No single benchmark tells the whole story.

Context window

The amount of text a model can consider at once, measured in tokens. A bigger window means the model can work with longer documents and conversations.

Fine-tuning

Further training of an existing model on specific data so it performs better at a particular job.

Hallucination

When a model states something false with confidence. Reduced by grounding answers in retrieved documents and by human review.

Inference

Running a trained model to get answers — as opposed to training, which is how the model was built.

Large Language ModelLLM

The core technology behind systems like Claude, ChatGPT and Gemini: a model trained on vast amounts of text to predict and generate language.

Model Context ProtocolMCP

An open standard that lets AI models connect to external tools and data sources in a consistent way.

Multimodal

A model that works with more than text — typically images, audio or video, as input, output or both.

Open weights

A model whose trained parameters are published so anyone can run or adapt it on their own machines.

Prompt

The instruction given to a model. Increasingly less important than the broader task definition for long-running agentic systems.

Reinforcement Learning from Human FeedbackRLHF

A training technique where humans rate model outputs and the model learns to prefer responses people judge as better.

Retrieval-Augmented GenerationRAG

A technique where a model first looks up relevant documents and then answers using them, improving accuracy.

Token

The unit models read and write — roughly three-quarters of a word in English. Pricing and context windows are measured in tokens.