The jargon, ticked off.

Every AI term you've nodded along to in a meeting, explained properly in a sentence or two. No maths degree required.

32 terms

Agent: A model given tools and a goal, allowed to loop: plan, act, observe, repeat, until the job is done.
Aspect ratio: The shape of the output frame. Set it deliberately — models compose very differently at 16:9 versus 1:1.
Chain of thought: Asking a model to reason step by step before answering. Slower, but much better on maths, logic and multi-step tasks.
Context window: How much text a model can hold in mind at once (prompt + reply). Go over it and the earliest parts drop off the edge.
Diffusion model: An image or video model that starts from noise and repeatedly denoises it into a picture guided by your prompt.
Distillation: Training a smaller, cheaper model to imitate a bigger one. Most of the quality, a fraction of the cost.
Embedding: A list of numbers representing meaning. Similar ideas land near each other, which is what makes semantic search work.
Few-shot prompting: Showing the model two or three worked examples so it copies the pattern instead of guessing at it.
Fine-tuning: Further training a base model on your own examples so it adopts a style or task without being told every time.
Generative art: Art produced by a system you design — code, rules or models — where the output is partly outside your direct control.
Guardrails: Checks around a model — input filtering, output validation, refusal rules — that stop bad responses reaching users.
Hallucination: When a model states something confidently wrong. It isn't lying — it's predicting plausible text with no fact-checker attached.
Inference: The act of actually running a trained model to get an answer. Training builds the brain, inference asks it a question.
Inpainting: Regenerating just a masked part of an image while leaving the rest untouched. Ideal for fixing hands and swapping details.
Keyframe: A anchor frame in a video generation that defines where a shot starts or ends; the model fills the motion between them.
Latent space: The compressed map of concepts a model has learned. Generation is really navigation through that space.
MCP: Model Context Protocol — a shared standard for connecting models to external tools and data sources.
Multimodal: A model that handles more than text — images, audio or video in, and sometimes out.
Negative prompt: A list of things you explicitly don't want in the output — common in image generation to kill artefacts and clichés.
Prompt engineering: Writing instructions that reliably get the output you want: clear role, clear task, clear format, clear constraints.
RAG: Retrieval-Augmented Generation: fetch relevant documents first, then ask the model to answer using only those. Grounds answers in your own data.
Rate limit: A cap on requests or tokens per minute. Hit it and you get a 429 — back off and retry rather than hammering.
Seed: The number that determines the random starting point. Same seed plus same prompt equals a reproducible image.
Streaming: Sending the reply token by token as it's generated, so the interface feels instant instead of frozen.
Structured output: Forcing the model to answer in a fixed shape, usually JSON matching a schema, so your code can parse it safely.
Style reference: An image fed alongside your prompt so the model borrows its look — palette, texture, lighting — for new subjects.
System prompt: The standing instructions a model reads before your message — persona, rules and boundaries for the whole conversation.
Temperature: A dial for randomness. Low means predictable and repetitive, high means creative and occasionally unhinged.
Token: The chunks a model reads and writes — roughly 4 characters of English. Pricing and limits are counted in tokens, not words.
Tool calling: Letting a model invoke your functions or APIs with structured arguments instead of just describing what should happen.
Upscaling: Increasing an image's resolution after generation, adding plausible detail rather than simply stretching pixels.
Vibe coding: Building software by describing what you want in natural language and iterating on what the AI produces, rather than typing every line yourself.

AI terms explained in plain English

AI comes with a lot of jargon: tokens, context windows, embeddings, RAG, fine-tuning, diffusion, hallucination. This glossary explains each term in a sentence or two, without maths or marketing language, so you can follow a product announcement, a technical doc or a meeting.

Search for any term or filter by category: AI basics, prompting, models, building or creative tools. It's free and kept up to date as new terms come into everyday use.