The Ultimate No-Judgment AI Glossary for Advertisers & Marketers

Your free, constantly updated, forever cheat sheet for staying smart (or at least sounding smart) when it comes to AI.

Let’s be honest: AI is moving at a ridiculous pace. One day, you’re learning what GPT stands for (it means “Generative Pre-trained Transformer” in case you were curious), the next you’re in a meeting being asked if your team’s prompt engineering is optimized for multimodal diffusion models. (Excuse me, what now?)

The truth? While everyone seems to be nodding confidently in meetings, many marketers (and agencies, and execs…) are likely still secretly Googling acronyms in between calls.

So, here’s your safety net: a constantly growing, no-shame, quick-reference glossary of the most important AI terms marketers and agency folks should know right now. Bookmark it. Use it before your next meeting. Pretend you always knew. We won’t tell.

🤖 Core AI Terms

• Artificial Intelligence (AI)
  A broad field of computer science focused on building machines that simulate human intelligence, like learning, reasoning, and problem-solving.

• Neural Network
  A model inspired by the human brain. It processes data in layers and is the foundation of many AI systems, including ChatGPT.

• Machine Learning (ML)
  A subset of AI that teaches computers to learn from data and improve over time without being explicitly programmed.

• Deep Learning
  A type of machine learning that uses layered neural networks (think: complex, brain-like structures) to process data. This is what powers image recognition, language models, and more.

• Large Language Model (LLM)
  A type of AI trained on massive amounts of text. It generates human-like language, used in tools like ChatGPT, Claude, and Gemini.

• Generative AI (GenAI)
  AI that can create: images, text, music, code, and more. Tools like ChatGPT, Midjourney, and Runway fall under this umbrella.

• Prompt(s)
  A broad term referencing the instructions, questions, or requests given to an AI system to guide its response or action. Usually conversational and can include images or document attachments depending on the AI model being used.

  • Example: “Rewrite this paragraph to sound more professional.”

• Query / Queries
  A more specific term referencing a prompt that is in question form. Typically used to extract actionable information from a database or knowledge base.

  • Example: “What is the correct ratio for Google Ads images?”

✍️ Copy, Content & Creative

• Prompt Engineering
  The art/science of crafting prompts to get better outputs from GenAI tools. Similar to targeting the right keywords and including them in your SEO, a small tweak can mean the difference between a rough draft and a golden ticket.

• Hallucination
  When an AI tool generates something that sounds legit, but isn’t true. (Yes, AI lies sometimes - always check your work!)

• Style Transfer
  Using AI to mimic the tone, voice, or visual style of existing content. Helpful for brand voice consistency or repurposing assets.

• Text-to-Image / Text-to-Video
  GenAI tools that turn prompts into visual or video content.

  • Example: “Create a pop-art style image of our mascot in space.”

• Content Automation
  Using AI to generate or assist with recurring content tasks like product descriptions, social captions, SEO blog drafts, or email subject lines.

📊 Marketing Ops & Tech Stack (You Might Hear These From Your Devs)

• Natural Language Processing (NLP)
  AI that understands, interprets, and generates human language. Essential for chatbots, voice-to-text tools, sentiment analysis, and AI copy tools.

• AI Co-Pilot
  A growing category of AI-powered assistants embedded in tools that help you write, summarize, and organize in context.

  • Examples: Google Workspace, Microsoft Copilot, or Notion AI

• Synthetic Data (See also: Data Noise / Noise)
  Artificially generated data that mimics real data and is used to train AI models or protect sensitive information.

• AI Model Training
  The process of teaching an AI model using data. Marketers don’t usually do this directly, but it helps to understand when people talk about "fine-tuning" a model for your brand.

• Data Noise / Noise
  Adding noise to data means introducing random, unpredictable values into a dataset. This can be done for various reasons, like trying to simulate the kind of imperfections you’d see in real-world data or to protect privacy if you're inputting sensitive information into an AI system.

• Diffusion Models
  These models learn to generate data by gradually adding noise (see data noise/noise) to a data sample until it becomes pure noise, and then learning to reverse this process to generate new data from the noise.

• Multimodal Diffusion Models
  A type of deep learning model that generates or manipulates data across different modalities (aka data types) such as text, images, audio, and video, by leveraging the principles of diffusion models (see diffusion models).

• Token
  A chunk of text used in AI processing.

📈 Data, Targeting & Optimization

• Predictive Analytics
  Using AI to analyze historical data and predict future behavior. Can be used to determine things like which leads are most likely to convert or which customers are likely to churn.

• Customer Segmentation

  Using AI to group customers based on behavior, preferences, or demographics. Often used for hyper-targeted campaigns.

• A/B Testing Automation
  Using AI to run and optimize multivariate tests faster and more efficiently than manual processes.

• Personalization Engines
  AI systems and tools that deliver individualized content or experiences (like product recommendations, dynamic email content, or custom landing pages) to users in real time.

⚠️ Ethics, Legal & the “OH NO” Stuff

• Bias in AI
  AI systems can reflect or amplify biases present in their training data, leading to ethical concerns or skewed outputs. Make sure you always sanity-check what AI generates.

• Deepfake
  Synthetic media (often video or audio) created, typically with AI, to look and sound like real people. Powerful, but often used to damage reputations or spread false information.

• Copyright & AI
  Current hot topic that, as of now, lies in a legal gray area. Many AI systems have been trained on massive amounts of text, images, sound, and video that come from a wide variety of sources (many of which have copyrights protecting them). The big questions here are: 1) Who owns AI-generated content? And 2) Can you train a model on copyrighted material? These answers are still evolving.

• Data Privacy
  If you're feeding AI tools sensitive customer info or proprietary data…proceed with caution. Many tools retain what you input.

Quick Tips for Advertisers & Marketers

• You don’t need to be a technical expert. You do need to be curious, strategic, and informed.

• Start small: test tools internally before rolling out client-facing solutions.

• Use AI to amplify human creativity, not replace it.

• Think of this as your AI cheat sheet. Bookmark it, love it, check back often.

Final Word

If you’ve felt behind on the AI conversation, you’re not alone. The tech is new, the pace is wild, and the learning curve is real. But knowledge is power, and you're already on the right track just by being here.

👋 Got a term we missed? Drop us a note. We'll keep this glossary growing as the landscape does.