An AI glossary for marketers: 21 jargons to know
What all the buzzwords actually mean.
You’re probably hearing a lot of AI jargon out there. You probably sort of know what some of them mean… but not really. And that’s OK.
Whether you're a CMO, content strategist, or the poor soul managing eight product launches, this AI guide for marketers is designed to keep you sounding smart without spending your weekends binging AI research videos on YouTube. Make sense of the 21 most common AI terms floating around your WhatsApp chats, team meetings, and LinkedIn feed.
1. Model
An AI model is like a super-powered brain in a box. You give it input (like a question or a photo), it processes it, and spits out a result. Think of the model as the engine that makes AI run.
There are different kinds:
LLMs (large language models) like ChatGPT, Gemini, and Claude: they process and generate text.
Image models like Midjourney or DALL•E: they generate images.
Video models like Sora or Veo: they create moving visuals.
Voice models like ElevenLabs: they replicate speech.
2. LLM (large language model)
These are AI models trained to understand and generate human-like text. Most can now also handle images, voice, and even videos, making them multi-modal. The "GPT-4o" model in ChatGPT? That "o" stands for "omni", because it can accept text, image, or audio inputs.
3. Transformer
No, not the robot kind. Transformers are a type of AI architecture developed by Google in 2017 that changed everything.
They introduced "attention," which lets the model understand relationships between words all at once, rather than one by one. It's why AI can now write poems, code apps, or help you brainstorm campaign taglines.
4. Training / Pre-training
Training is how an AI model learns. Developers feed it billions of words from books, websites, Reddit threads, movie subtitles—you name it. The model tries to predict the next word in a sentence, gets feedback, and adjusts. Over time, it gets really, really good at language.
5. Supervised learning
When a model is trained with labelled examples (e.g. this email = spam, this one = not spam), it’s called supervised learning. The model learns from examples where the "right answer" is known.
6. Unsupervised learning
No labels here: the model has to figure things out itself. Unsupervised learning is great for grouping similar things or spotting anomalies (e.g. fraud detection, market segmentation).
7. Post-training
This is the polishing phase. Once a model finishes pre-training, developers fine-tune it for specific use cases or industries (e.g. medicine, law, marketing).
8. Fine-tuning
You take a general-purpose model and give it a crash course in your own data. Want it to write in your brand tone? Train it on your emails, blogs, and Slack messages.
9. RLHF (reinforcement learning from human feedback)
AI doesn't always do what we want. RLHF fixes that. Humans rank outputs ("this response is better than that one"), and the model learns what people prefer. It’s why ChatGPT feels more helpful and polite than a raw language model.
10. Prompt engineering
How you talk to an AI matters. Prompt engineering is the skill of crafting inputs that produce the best responses.
"Write me a tagline for an AI-powered instant noodle brand" might work. "Act as a creative director. Pitch 5 witty, Gen Z-style taglines for an AI-enabled ramen brand" will work better.
11. RAG (retrieval-augmented generation)
Models can sometimes forget things or make stuff up. RAG helps prevent that. It lets the model search your documents, databases, or websites for relevant info before answering you. Think of it as giving the model Google access in real time.
12. Inference
This is when the model is "running." You send it a prompt, it replies. That's inference.
13. MCP (model context protocol)
MCP is an emerging standard that lets AI models talk to your apps—calendar, Slack, HubSpot, even GitHub. Instead of you copying and pasting data, the model can fetch it and act on it. Magic.
14. Hallucination
When AI just makes things up. Common with LLMs. You ask for facts, it gives you fiction. RAG helps reduce hallucinations. Always double-check your AI-sourced insights.
15. Token
AI doesn’t read text like humans. It reads chunks called tokens. "AI is cool" = 3 words = 4 tokens. Most models have token limits, so keep that in mind for long documents.
16. Latency
The delay between your question and the AI’s answer. Faster models = lower latency. Important if you want real-time customer support bots or campaign testing assistants.
17. Fine-tuning vs RAG
Confused between the two? Think of it like this:
Fine-tuning = teach the model new knowledge permanently
RAG = give it temporary access to relevant info
Want your AI to always know your company values? Fine-tune it. Want it to pull the latest sales numbers? Use RAG.
18. Multimodal
This means the model can work with more than one type of input: text, image, voice, video. GPT-4o, Gemini 1.5, and Claude 3.5 all support this now.
19. API
Stands for Application Programming Interface. Think of it as a pipe that lets your website or app access the AI model programmatically. Marketers use APIs to build chatbots, automate email writing, or personalise landing pages.
20. Agent
An AI agent isn’t just answering one prompt—it’s working toward a goal. Agents can complete multi-step tasks like:
Pulling data
Making a deck
Writing an email
Sending it via Gmail
AutoGPT, OpenDevin, and ChatGPT agents are early examples.
21. Vector database
A special kind of database that stores data in the form AI models understand: numbers. Super useful for search and RAG systems, especially when trying to match questions with answers.
Final thoughts
Feeling overwhelmed by all of these? Don't stress. The goal isn’t to become an AI engineer overnight, but to build a working vocabulary so marketers like you can ask smart questions, scope the right projects, and avoid being bamboozled by AI buzzwords in the next strategy meeting.
Bookmark this glossary, share it with your team, and remember: you don’t need to know everything about AI to start using it effectively—you just need to know enough to be dangerous.