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url: /ai/glossary/
title: "AI & Design Glossary | The Graphic Design School"
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lastModified: 2026-06-22T06:00:37.105Z
category: ai-tools
site: "The Graphic Design School"
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# AI & Design Glossary | The Graphic Design School

AI & Design Glossary Plain-language definitions of the AI terms that show up in design work, written for what they mean at your desk, not for the maths underneath. Every entry here is defined through a design lens: what the term changes about your work, when you would reach for it, and where to see it in practice. Terms are grouped by the part of the process they belong to and listed alphabetically within each group. Where a term carries a legal or ethical weight, the definition says so plainly rather than waving it away. Image Generation CFG scale How strictly the model obeys your prompt. Push it up and the model sticks closely to what you asked, sometimes stiffly. Pull it down and it takes more liberties. When an image ignores half your brief, this is usually the dial to reach for before you rewrite the prompt. See in practice: Image Generation Tools · Brief Writing with AI · Build Taste, Generate, Refine ControlNet An add-on that lets you steer a generation with a structural guide: a sketch, a pose, an edge map, a depth map. It is how you keep a composition you have decided on while letting the model handle surface and style. Structure from you, rendering from the model. See in practice: Image Generation Tools · Composition and AI · Build Taste, Generate, Refine diffusion model The kind of model behind Midjourney, DALL-E, and Stable Diffusion. It starts from visual noise and clears it away step by step until an image appears. You do not need the maths, but it helps to know the output is grown, not retrieved: there is no stock photo underneath, which is exactly why your prompt and references matter so much. See in practice: Image Generation Tools · The Design Advantage · Build Taste, Generate, Refine img2img Feeding the model an existing image and asking it to reimagine that image in a new style, medium, or treatment, rather than starting from text alone. A fast way to carry a composition you like into a different visual register. See in practice: Image Generation Tools · Moodboarding with AI · Build Taste, Generate, Refine inpainting Redrawing one chosen part of an image while leaving the rest untouched. You mask a region and regenerate only inside it. This is the workhorse of refinement: fix a hand, swap a background object, or correct a detail without losing the parts that already work. See in practice: Image Generation Tools · Build Taste, Generate, Refine · From Prompt to Portfolio latent space The internal map a model uses to organise visual ideas before it draws anything. Concepts that look related to the model sit near each other there. The practical takeaway: small wording changes can move you a long way across that map, which is why two near-identical prompts sometimes return wildly different images. See in practice: Image Generation Tools · Build Taste, Generate, Refine · The Design Advantage LoRA A small add-on that teaches a model one specific look, character, or style without retraining the whole thing. For a designer it is the closest tool to a reusable brand filter: train it once on a consistent set of references and apply that visual signature across many generations. See in practice: Image Generation Tools · Moodboarding with AI · Colour Theory and AI outpainting Extending an image past its original edges, as if panning the camera to reveal more of the scene. Useful when a composition is strong but cropped too tight, or when you need a wider format for layout without regenerating the whole piece. See in practice: Image Generation Tools · Composition and AI · Build Taste, Generate, Refine prompt weight Syntax that tells the model which words to care about most. Increasing the weight on a word pushes the model to honour it; decreasing it lets the word fade. A precise tool for when an image is right except for one element the model keeps underplaying. See in practice: Image Generation Tools · Brief Writing with AI · Visual Hierarchy and AI sampler The method a model uses to clear away noise as it generates. Different samplers carry slightly different aesthetic fingerprints, some cleaner, some grainier. You rarely change it early on, but it is worth knowing it exists when two runs of the same prompt feel different for no obvious reason. See in practice: Image Generation Tools · Build Taste, Generate, Refine seed The starting number that fixes a generation. Reuse the same seed with the same prompt and you get the same image back. This is your handle on consistency: lock a seed when you have something close and want to change one thing at a time instead of rerolling into a different picture. See in practice: Image Generation Tools · Build Taste, Generate, Refine · From Prompt to Portfolio steps How many passes the model takes to refine an image. More passes add detail up to a point, then stop helping and just cost time. Past roughly twenty to fifty you are usually paying for patience, not quality, so treat it as a tradeoff, not a quality slider. See in practice: Image Generation Tools · Build Taste, Generate, Refine upscaling Enlarging a low-resolution image while keeping its detail clean, often adding texture the original lacked. It belongs at the end of a workflow, once you have settled on a final composition, not while you are still exploring, because the cost adds up fast on images you will discard. See in practice: Image Generation Tools · From Prompt to Portfolio · Research & Ideation Tools Language Models & Prompting agent A model that does more than chat: it plans a few steps and takes actions, like searching, building, or assembling a result, to reach a goal. Increasingly common in layout and prototyping tools that turn a prompt into a working draft you then direct. See in practice: Layout & Prototyping Tools · From Prompt to Portfolio · Text & Copy Tools chain-of-thought Asking a model to reason step by step before it answers, rather than jumping straight to a conclusion. For design critique and brief analysis this reliably produces more specific, more useful output than a one-line request. See in practice: Design Critique with AI · Text & Copy Tools · Brief Writing with AI completion The text a model generates to continue from your prompt. Give it the opening of a sentence or a brief and it carries on. The most basic shape of working with a language model, underneath every chat interface. See in practice: Text & Copy Tools · Brief Writing with AI · How to Use ChatGPT for Design context window How much conversation and material a model can hold in mind at once, measured in tokens. Go past it and the earliest details quietly drop. For design work this is why a long, rambling chat starts forgetting your brief: feed it the essentials, not the whole history. See in practice: Text & Copy Tools · Brief Writing with AI · How to Use ChatGPT for Design embedding Turning text or images into a string of numbers that captures their meaning, so a machine can judge what is similar to what. The quiet engine behind visual search and reference recommendation, where you find images by feel rather than by keyword. See in practice: Research & Ideation Tools · Moodboarding with AI · Image Generation Tools few-shot Teaching by example. Show the model two to five worked input-output pairs before asking for a new one, and its results get far more consistent. The fastest way to pin down a format or tone without writing a paragraph of instructions. See in practice: Text & Copy Tools · Brief Writing with AI · Design Critique with AI fine-tuning Training a model further on your own material so it specialises in your voice or domain. It is slower and more costly than prompting, and for most design work a good brief and a few examples get you most of the way without it. See in practice: Text & Copy Tools · Brief Writing with AI · The Design Advantage hallucination When a model states something false with complete confidence. It is not lying, it is filling a gap with a plausible guess. The confident-looking errors are the dangerous ones, which is why a working eye and a fact-check matter most exactly where the output looks most finished. See in practice: When AI Gets It Wrong · Design Critique with AI · Text & Copy Tools RAG Retrieval-Augmented Generation: handing a model the real documents it should answer from, so its reply is grounded in your material rather than its training. The reliable way to get answers about a specific client, brand, or brief instead of a generic best guess. See in practice: Text & Copy Tools · Brief Writing with AI · Research & Ideation Tools system prompt The standing instruction that sets a model’s role and limits before your conversation begins, like casting direction before a shoot. In most consumer tools you never see it, but in tools that let you set one it is the single most effective place to fix tone and behaviour. See in practice: Text & Copy Tools · Brief Writing with AI · Design Critique with AI temperature A setting that governs how predictable a model’s output is. Low keeps it tight and repeatable; high makes it loose and surprising. Reach for low when you want a reliable format, high when you are fishing for unexpected directions early in a project. See in practice: Text & Copy Tools · Build Taste, Generate, Refine · Research & Ideation Tools token The small chunk a model breaks text into before it reads it. A word can be several tokens. You care about this for two practical reasons: it sets what a model costs to run, and it sets how much it can hold in mind at once. See in practice: Text & Copy Tools · Brief Writing with AI · How to Use ChatGPT for Design tool use When a model reaches outside the conversation to call a function, an API, or a design tool, deciding for itself when to do so. It is what lets an assistant fetch live information or build something rather than only describe it. See in practice: Layout & Prototyping Tools · Text & Copy Tools · Client Disclosure Design & AI Workflow AI moodboard A reference collection of images, colours, and directions assembled quickly with AI to capture a mood before you commit. It speeds up the part of the process where you decide what good looks like, which is the part that most shapes the final work. See in practice: Moodboarding with AI · Build Taste, Generate, Refine · Research & Ideation Tools AI-assisted mockup Building a draft layout yourself, then letting the model fill backgrounds, draft copy, or stand in placeholder images. A hybrid workflow where the structure and decisions stay yours and the model handles the filling-in. See in practice: Layout & Prototyping Tools · From Prompt to Portfolio · Build Taste, Generate, Refine aspect ratio The proportions of your final image, such as 16:9, 1:1, or 9:16. Decide it before you generate, not after, or you will waste runs cropping into compositions the model never framed for that shape. See in practice: Composition and AI · Build Taste, Generate, Refine · Image Generation Tools brand lock The set of constraints that keep AI-generated work on-brand: fixed logo placement, an allowed colour range, type rules. Without them, generation drifts off-brand fast; with them, you can move quickly and still stay consistent. See in practice: Colour Theory and AI · Build Taste, Generate, Refine · Layout & Prototyping Tools composition constraint A structural rule you give the model, such as rule-of-thirds, asymmetrical balance, or a centred focal point. It guides the layout without dictating every pixel, and it is the lever that pulls a generation out of the model’s default centred symmetry. See in practice: Composition and AI · Visual Hierarchy and AI · Build Taste, Generate, Refine generative fill Selecting empty or unwanted space in an image editor and having the model paint in something that fits, whether to extend a background or remove an object. A precise, in-context tool that keeps you inside your working file. See in practice: Layout & Prototyping Tools · Moodboarding with AI · Image Generation Tools iteration loop The cycle of generate, critique, refine, run fast enough that you can explore ten directions in the time it once took to sketch one. The speed is the point only if your critique is good: it multiplies whatever judgement you bring to each pass. See in practice: Build Taste, Generate, Refine · Design Critique with AI · From Prompt to Portfolio prompt template A reusable instruction with blanks you swap out, such as redesign this object in this style for this audience. It saves you rewriting the same scaffolding and, more usefully, makes your prompting repeatable and reviewable. See in practice: Text & Copy Tools · Brief Writing with AI · Build Taste, Generate, Refine reference image A picture you hand the model to anchor what you want, whether for style, composition, or colour. Often worth more than a paragraph of description, because it tells the model what you mean instead of what you can spell out. See in practice: Moodboarding with AI · Build Taste, Generate, Refine · Image Generation Tools style transfer Applying the visual style of one image to the content of another, so a photo can take on a painting’s treatment or a brand’s palette. Useful for testing a direction quickly, with the usual caution about whose style you are borrowing. See in practice: Moodboarding with AI · Colour Theory and AI · Copyright and Licensing variation batch Generating several versions of a design at once, then choosing among them rather than chasing one perfect result. A good fit for early exploration, where seeing four real options teaches you more than imagining one. See in practice: Build Taste, Generate, Refine · Layout & Prototyping Tools · Moodboarding with AI vectorising Converting a pixel image into editable vector paths, so it scales to any size without going soft. AI tools automate the path-drawing that used to be slow and manual, which matters whenever a raster generation needs to become a logo or print-ready mark. See in practice: Image Generation Tools · Layout & Prototyping Tools · Composition and AI voice clone Synthesising audio in a particular person’s voice, used for narration or multilingual voiceover in a consistent tone. Powerful and sensitive in equal measure: using someone’s voice is a consent and disclosure question, not just a technical one. See in practice: Client Disclosure · Research & Ideation Tools · When AI Gets It Wrong Ethics, Copyright & Provenance AI-assisted label A plain badge, such as partially AI-generated, on work that mixes your craft with AI tools. A growing best practice in portfolios and client work, and a simple way to be transparent without underselling the judgement you brought. See in practice: Client Disclosure · From Prompt to Portfolio · Copyright and Licensing attribution Crediting the sources behind a piece of work. With AI the honest version is twofold: respect the rights in any source material, and be transparent that AI was involved when it matters to the client or audience. See in practice: Client Disclosure · Copyright and Licensing · When AI Gets It Wrong C2PA The Coalition for Content Provenance and Authenticity: a standard for attaching a signed record of who created and edited an image. Adoption is still early, but it is the most likely path to provenance you can actually verify. See in practice: Copyright and Licensing · Client Disclosure copyright holder The person or entity that owns a work’s exclusive rights. For AI output, most jurisdictions vest copyright in a human, but ownership of anything derived from training material remains contested. When the holder is unclear, so is your right to use the work. See in practice: Copyright and Licensing · Client Disclosure · When AI Gets It Wrong derivative work A new creation built on existing copyrighted material. An AI image clearly modelled on a known artist or painting may count, and ownership of such work is far from settled. The four-factor matrix in the copyright article is how you reason about the risk. See in practice: Copyright and Licensing · Client Disclosure · When AI Gets It Wrong fair use A legal doctrine allowing limited reuse of copyrighted work for things like commentary, criticism, or teaching. It is fuzzy in court and especially untested for AI, so treat it as a question for a lawyer, not a licence you can assume. See in practice: Copyright and Licensing · Client Disclosure · When AI Gets It Wrong indemnification A vendor’s promise to cover your legal costs if their tool’s output lands you in an infringement claim. Some AI vendors offer it, many do not, and the difference belongs in your decision about which tool to put near paying client work. Check the terms. See in practice: Copyright and Licensing · Client Disclosure model license The terms that say how you are allowed to use a model: commercial work, research only, or open to modify. They vary widely between tools, and reading them is the difference between a clearance you can defend and one you are assuming. See in practice: Copyright and Licensing · Image Generation Tools · Client Disclosure opt-out A mechanism for keeping your own work out of AI training sets. Registries and tools exist to claim your images, though enforcement is still young. Worth knowing both as a creator protecting your work and as a sign of how a vendor treats artists. See in practice: Copyright and Licensing · Client Disclosure provenance The documented origin and edit history of a piece of work: who made it, when, and how. It underpins copyright claims, and because AI output rarely carries clear provenance, building your own record of decisions becomes part of professional practice. See in practice: Copyright and Licensing · From Prompt to Portfolio · Client Disclosure synthetic media disclosure Labelling content as AI-generated or altered. Platforms increasingly require it, and even where they do not, disclosing builds trust. The legal floor is rising, but the professional case for honesty was always there. See in practice: Client Disclosure · Copyright and Licensing · When AI Gets It Wrong training data The vast set of images and text a model learned from. Because the sources are often unclear, so is the legal footing of what comes out. Vendors increasingly disclose their data or offer an opt-out, and knowing a tool’s data story is part of using it responsibly. See in practice: Copyright and Licensing · When AI Gets It Wrong · The Design Advantage watermark A visible or hidden mark that signals ownership or origin. AI tools often strip ordinary watermarks, which is why cryptographic standards like C2PA are emerging to carry that information in a form that survives editing. See in practice: Copyright and Licensing · Client Disclosure · When AI Gets It Wrong Every term here is a tool, and a tool is only as good as the judgement you bring to it. The vocabulary that decides whether an AI output is any good is the same design vocabulary it always was. That is where Design Knowledge & AI starts.