AI Terms for Marketers

Forty four core concepts in seven groups, every definition with the marketing consequence attached.

There is no shortage of AI glossaries. What they share is a habit of defining terms the way a computer scientist would, technically correct and practically useless, because they never tell you what the term means for your budget, your brand or your Tuesday. This page takes the opposite approach. Every concept here is explained the way one marketer would explain it to another, with the analogy drawn from work you already do and the consequence stated for the job you actually have.

Use it two ways. Read it once top to bottom, the groups are sequenced so each builds on the last. Then keep it open in vendor calls, because the moment a term on this page gets used to impress rather than inform, you will notice, and noticing is the point.

Showing all 44 terms

The family tree

Vendors use these six terms as if they were interchangeable. They are not, they nest inside each other, and knowing the nesting is what lets you ask what a product actually does.

Artificial Intelligence (AI) #

The umbrella term for machines doing work that normally needs human intelligence, understanding language, spotting patterns, making decisions. When a vendor says their product 'has AI', they have told you as much as a restaurant saying its food 'has ingredients'. Your follow-up question is always which kind, doing what.

Machine Learning (ML) #

The branch of AI where a system learns patterns from examples instead of following hand-written rules. Your spam filter is machine learning, so is the algorithm deciding which of your ads to show whom. Most of the 'AI' that has quietly run marketing platforms for a decade is this.

Deep Learning #

Machine learning built on neural networks with many layers, which is what made modern AI possible. You will hear it in pitches as a credibility word. The marketing translation is simply 'the powerful kind of machine learning', and the term alone tells you nothing about whether the product is good.

Neural Network #

The architecture underneath deep learning, loosely inspired by how brains connect neurons. Think of it as the plumbing. You do not need to understand it any more than you need to understand your CMS database schema, you just need to know the word so it cannot be used to end a conversation.

Natural Language Processing (NLP) #

The branch of AI that works with human language, reading it, understanding it, generating it. Every sentiment analysis tool, every chatbot and every writing assistant you have used sits here. LLMs are NLP's breakout success story.

Generative AI #

AI that creates new content, text, images, audio, video, rather than just sorting or scoring what exists. The line matters commercially, analytical AI reads your campaign data, generative AI writes your campaign. Different risks, different gates, different budget lines.

The engine room

How the tools you actually use work under the bonnet. These eight terms explain the behaviour you see every day, the pricing you pay, and the limits you keep bumping into.

Large Language Model (LLM) #

The engine behind Claude, ChatGPT and Gemini, trained on enormous volumes of text to predict what comes next in a sequence. It is a prediction machine, not a knowledge database, and that one distinction explains both why it writes beautifully and why it cannot be trusted with your product's launch date.

Model #

A specific trained system with its own strengths, personality and price point. Models differ the way agencies differ, choosing one is a fit decision against your work, not a spec-sheet comparison. Pilot with your own briefs before you commit.

Training data #

The text a model learned from, absorbed as patterns rather than memorised as a library. This is why it writes fluently about your category yet gets your founding year wrong, and it is also where a model's blind spots and biases come from. The corpus is the character.

Token #

The unit a model reads and writes in, roughly three quarters of a word in English. Tokens are the currency of AI, pricing, speed and length limits are all counted in them, so when procurement asks what the tool costs, the honest answer is 'it depends how much we make it read and write'.

Context window #

The model's working memory, everything it can hold in view at once, your brief, your documents, the conversation so far. When the window fills, the model loses the thread, which is why long chats drift off-voice and why a two-hundred-page brand book cannot just be pasted in and remembered forever.

Knowledge cutoff #

The date a model's training data ends. Anything after that date does not exist for it unless it can search the web or you hand it the information. For a function that lives on current campaigns, competitor moves and this quarter's numbers, this is not a footnote, it is a workflow design constraint.

Inference #

The moment a trained model generates a response, as opposed to training, which happened months earlier at someone else's expense. Every AI feature in your stack is an inference cost to somebody, which is why 'unlimited AI' pricing tends not to stay unlimited.

Temperature #

The dial between predictable and adventurous output. Low temperature suits product descriptions and compliance copy where consistency wins, higher temperature suits ideation where you want range. When a tool offers a 'creativity' slider, this is the machinery behind it.

Directing the machine

Briefing an AI is a skill, and conveniently it is a skill marketers already have, because a good prompt is structurally a good creative brief.

Prompt #

The instruction you give the model. Treat it exactly like an agency brief, context, audience, constraints, examples of good, and the output quality tracks the brief quality with uncanny fidelity. Vague brief in, generic work out, at machine speed.

System prompt #

The standing instruction that shapes a model's behaviour before your message ever arrives, the persona, the rules, the boundaries. It is the retainer agreement to your prompt's individual brief. When an AI tool feels opinionated or oddly constrained, the system prompt is usually why.

Prompt engineering #

The craft of structuring instructions so the model performs reliably, less mystical than the job title suggests. For marketers it is closer to briefing discipline than to programming, and the teams that write good creative briefs turn out to write good prompts.

Few-shot prompting #

Showing the model two or three examples of what good looks like before asking for output. It is the swipe file principle applied to machines, and it remains the single cheapest quality upgrade available. This is exactly why a documented voice corpus outperforms any clever one-line instruction.

Chain of thought #

Asking the model to reason through intermediate steps before answering, the way you would ask a junior strategist to show their working. It slows the answer down and measurably improves it on anything with logic in it, budget allocations, funnel maths, campaign sequencing.

The trust layer

The terms that decide whether an output is publishable. This is the section to actually memorise, because this is where marketing careers meet AI risk.

Hallucination #

The model stating something false with complete confidence, an invented statistic, a fabricated quote, a plausible-sounding source that does not exist. It happens because prediction machines generate what is likely, not what is verified, so it is a structural feature, not a bug awaiting a patch. Human review of factual claims is therefore permanent, not transitional.

AI bias #

Skew inherited from training data that shows up in output, in who gets depicted, which markets get assumed as default, whose English counts as neutral. For global marketing this is a brand safety issue wearing a technical name, and it is why regional review gates exist.

Grounding #

Anchoring the model's answers in source material you supplied rather than its training memory. A grounded model cites what you gave it, an ungrounded one improvises around the gaps, and the difference is the difference between research and rumour with better formatting.

RAG (Retrieval-Augmented Generation) #

The architecture behind grounding, the system fetches relevant documents first, then the model answers from them. When a vendor says their AI 'knows your knowledge base' or 'is trained on your content', RAG is almost always what they actually mean, and the distinction is worth pressing on in the demo.

Fine-tuning #

Retraining a model on your own data so desired behaviour is built in rather than prompted in. It is the bespoke suit of AI, impressive, expensive and rarely necessary, because for most marketing teams good prompting plus good context gets ninety percent of the way at a fraction of the cost.

Guardrails #

The constraints built around a model to keep output inside defined bounds, banned topics, required disclaimers, tone limits, claim restrictions. Here is the reframe that matters, your brand guidelines become guardrails the moment you write them down in a form a machine can follow. Undocumented standards protect nothing.

Human in the loop (HITL) #

An operating model where AI produces and a human decides, with named review gates before anything consequential ships. The evidence keeps landing the same way, teams that build review into the workflow outperform both full automation and pure human production. It is the model Distill runs on, and the subject of /ai-in-gtmplaybooks.

Search and being found

AI is rewriting how buyers discover you before you ever get a click. This group is the new SEO conversation, and it is moving faster than the agencies selling it.

AI Overviews #

The AI-generated answer that now sits at the top of Google results, assembled from sources it deems credible. The consequence is structural, your content can inform the answer a buyer reads without earning the visit, which changes what ranking is worth and what winning looks like.

Zero-click search #

A search resolved on the results page itself, no visit to anyone's site. It predates AI but AI answers are accelerating it sharply, so visibility and traffic are decoupling, and your measurement model needs to catch up before your traffic report scares the wrong stakeholder.

GEO / AEO (Generative Engine Optimization, Answer Engine Optimization) #

Two near-interchangeable names for the same emerging craft, making your content the source AI assistants cite when they answer questions in your category. The early signals favour clear structure, sourced claims, named entities and genuine expertise, which is convenient, because that was always good content strategy wearing a new acronym.

AI crawlers #

The bots that collect web content for AI training and AI answers, GPTBot and ClaudeBot being the best known. Whether to allow them is now a genuine strategic decision, block them and you protect your content, allow them and you exist in the answers your buyers are reading. There is no neutral choice.

Semantic search and embeddings #

Search by meaning rather than by keyword match, powered by embeddings, which represent text as coordinates of meaning so that 'reduce churn' and 'improve retention' land in the same neighbourhood. This is why keyword-stuffing is dead weight and why writing that says what it means, in the buyer's language, keeps winning.

Content and creative production

The generative wave hit content and design first, so this is where a marketer's vocabulary needs the most updating, on both the opportunity side and the risk side.

AI slop #

The flood of low-effort, mass-produced AI content now filling feeds, inboxes and search results. It matters twice over, platforms are actively learning to suppress it, and audiences are learning to smell it, which makes distinctive human judgment in your content the appreciating asset in a depreciating market.

Diffusion model (text-to-image) #

The technology behind image generators like Midjourney, DALL-E and Firefly, which build an image from noise, guided by your prompt. Superb for concepts, moods and internal decks, still genuinely hard on brand consistency, exact products and text rendering, so treat it as an ideation partner before a production line.

Text-to-video #

The same generative leap applied to moving image, and the fastest-moving frontier in the creative stack. The near-term marketing use is storyboards, animatics and pre-visualisation at a fraction of historical cost, while the final brand film remains a human production for now.

Synthetic media and deepfakes #

Synthetic media is any AI-generated audio, image or video, deepfakes are the subset that imitates a real person. For marketers this is both a tool and a threat surface, the same technology that localises your spokesperson into six languages can impersonate your CEO in a payment scam, so verification protocols are now a brand function.

Voice cloning #

Generating a specific human voice from recorded samples, now good enough for podcast and audio production at scale. The legitimate uses are real and growing, the governing rules are consent and disclosure, and any vendor vague on either is telling you something.

Content provenance and watermarking #

Emerging standards, C2PA being the main one, that attach origin information to content, marking what was AI-generated and how. Platforms and ad networks are beginning to require AI disclosure labels, so provenance is quietly becoming a compliance question, not just an ethics one.

AI detectors #

Tools claiming to identify AI-written text. Their accuracy is poor and their false positives are notorious, flagging fluent human writing as machine-made and missing lightly edited machine text. Build your quality policy on editorial standards and named review, not on a detector's verdict.

In your stack

Where AI actually shows up in a marketer's toolkit, and the vocabulary of the vendor conversations coming your way this year.

API #

The connection that lets one piece of software talk to another, which is how AI ends up inside your CRM, your email platform and your analytics suite. When a tool announces 'new AI features', it is very often an API call to one of the major models underneath, which is a fair question to ask at renewal time when the AI line item appears.

Agent (agentic AI) #

AI that takes multi-step actions toward a goal, researching, drafting, updating systems, rather than answering a single prompt and stopping. The capability is real and moving fast, and its arrival raises the stakes on one thing above all, having your approval gates defined before the autonomy shows up, not after.

Multimodal #

A model that works across formats, reading images, hearing audio, producing both, not just text. This is why one assistant can now critique a layout, transcribe a customer call and draft the follow-up sequence in a single conversation, and why 'text tool' is already an outdated mental category.

Sentiment analysis #

Using NLP to read the emotional tone behind text at scale, reviews, social mentions, survey verbatims. It is the oldest AI most marketers already own, useful for direction and volume, blunter than a human reader on sarcasm, code-switching and culturally specific expression, so treat the dashboard as a compass rather than a verdict.

Predictive analytics #

Machine learning applied to historical data to forecast what happens next, churn risk, lead scoring, next best action. The quiet workhorse of martech. Its honest limitation is that it predicts the future from the past, which is precisely when it fails, at the moments the market genuinely changes.

Automation vs AI #

Automation follows rules a human wrote, if this, then that. AI makes judgment calls a human did not explicitly script. The distinction decides your governance, automated workflows need testing, AI outputs need review, and a great deal of vendor marketing works hard to blur exactly this line.

No term matches that search.
If it belongs on this page, tell us, this sheet is curated, not finished.

The week in AI, distilled

This page covers the vocabulary. The weekly briefing covers what is happening right now, five items a week, each with why a marketer should care. It ships inside Clarity, the Distill newsletter.

Subscribe to Clarity