A Plain-English Glossary of AI Terms

Jargon can make AI feel harder than it is. Here are common terms in everyday language.

• AI (artificial intelligence): software that performs tasks that normally need human intelligence.
• Machine learning: systems that learn patterns from data instead of following only fixed rules.
• Deep learning: machine learning using many-layered neural networks.
• Generative AI: AI that creates new text, images, audio, video or code.
• Model: the trained system that produces outputs.
• Algorithm: a set of steps for solving a problem.

• LLM (large language model): a model trained on lots of text to predict and generate language.
• Token: a chunk of text the model processes.
• Context window: how much text the model can consider at once.
• Prompt: the instructions or question you give.
• Hallucination: a confident but false or invented output.
• Temperature: a setting controlling how varied or predictable outputs are.

• Training: teaching a model from data.
• Fine-tuning: further training for a specific task or style.
• RAG (retrieval-augmented generation): looking up documents to ground answers.
• Embedding: a numerical representation capturing meaning.
• Inference: running a trained model to get an answer.
• Parameters: the adjustable values learned in training.
• Benchmark: a standard test of model performance.

• Agent: AI that plans and takes actions using tools.
• Tool use / function calling: a model requesting actions like search or calculations.
• API: a way for software to connect to a service.
• Prompt injection: hidden instructions that try to hijack an AI.

• Bias: systematic unfairness in outputs.
• Deepfake: realistic fake image, audio or video.
• Guardrails: safeguards that limit harmful behaviour.
• Alignment: making AI behave in line with human intentions and values.
• Explainability: how well we can understand why a model gave an answer.
• Provenance: records of where content came from.

• GPU: a processor suited to parallel calculations used in AI.
• NPU: a chip in devices for AI tasks.
• On-device AI: models running locally rather than in the cloud.

You don't need to memorise everything. Knowing these terms makes it easier to ask good questions and cut through hype.