AI may be the biggest shift in marketing since the rise of SEO, but it sure isn’t making our jobs any easier to talk about.
With every new tool or vendor pitch comes a fresh wave of acronyms and jargon. RAG? LLMO? Embeddings? For busy marketers, trying to parse it all can feel less like keeping up with innovation and more like cramming for a final you didn’t know was on the calendar.
But learning the lingo isn’t just useful for sounding smart in meetings. It’s can help you make sharper bets on strategy, spot vendor BS from a mile away, and build workflows that actually, well, work. That’s where this glossary comes in.
We’ve decoded the most relevant AI terms for marketers — what they mean, why they matter, and how to put them into practice. Skim it, bookmark it, or bring it to your next budget meeting. (We won’t tell.)
1. Model Mechanics
Large Language Model (LLM)
What it is: Large language models are AI systems trained on vast amounts of text data to understand and generate human-like language. They power chatbots, content generators, and many AI writing tools.
Why marketers care: LLMs enable automated content creation, summarization, and customer interaction at scale.
Example: A content team uses an LLM-based tool (e.g. ChatGPT or Claude) to draft blog outlines and social posts, speeding up production while maintaining brand voice consistency.
Transformer Architecture
What it is: The neural-network design behind most modern LLMs. Its “attention” mechanism lets the model weigh the relationships between words, often resulting in more coherent output.
Why marketers care: Tools built on transformers usually write cleaner copy than older models. Asking vendors if they use transformer tech is a simple quality check.
Example: A B2B publisher upgrades an aging summarization engine to a transformer-based API and cuts manual editing time in half.
Parameters vs. Tokens
What they are: Parameters are the model’s internal weights, learned during training, that define its “knowledge” and behavior. They don’t change after training (unless fine-tuned… more on that in a sec). Tokens, on the other hand, are units of text like words, subwords, or characters processed during input/output. These are used for billing and context length.
Why marketers care: Pricing for many AI APIs is based on tokens, and higher parameter counts often mean deeper context — but also higher cost.
Example: A demand-gen manager trims prompts to stay under 1,000 tokens per request, reducing monthly API spend without hurting output quality.
Fine-Tuning
What it is: Adapting a pre-trained model to your brand voice or industry jargon by giving it a small, custom dataset.
Why marketers care: A fine-tuned model produces on-brand copy that needs fewer edits.
Example: A cybersecurity company fine-tunes an LLM on past research reports so product pages sound like they were written by in-house experts.
Multimodal
What it is: AI that can work with more than one data type — text, images, audio, or video — inside the same model.
Why marketers care: Multimodal tools can create social images from copy or write captions for user-generated photos. This can save time across channels, as well as aid in repurposing one format (an e-book or webinar) into another (social content or a podcast).
Example: A retailer uses a multimodal model to turn product specs into short promo videos with voiceover and subtitles.
Hallucinations
What it is: Confident-sounding but incorrect content generated by an AI model.
Why marketers care: A single bogus stat can cause audience trust to nosedive — and yes, people do notice. Don’t be the brand that confidently invents GDP numbers or fake Gartner citations.
Example: A finance blog adds a “human-in-the-loop” review step to catch hallucinated numbers before posts go live.
2. AI for Content Workflows
Prompt Engineering
What it is: Writing clear, detailed instructions that steer an AI model toward the output you want.
Why marketers care: Better prompts mean fewer rewrites and lower costs.
Example: A social media lead adds role, tone, and length details to prompts, boosting usable first drafts from 30% to 80%.
Prompt Chaining
What it is: Linking prompts so the output of one becomes the input of the next. An example: research → outline → draft.
Why marketers care: Chaining keeps complex tasks organized and reduces errors.
Example: A podcast team chains prompts to transcribe episodes, pull key quotes, and draft newsletter blurbs in under an hour.
Agentic Workflows/AI Agents
What it is: AI agents are semi-autonomous systems that can handle multi-step tasks, interact with tools, and adjust based on feedback — though most still need human oversight to stay on track.
Why marketers care: Agents can monitor trends, draft content, and even schedule posts, freeing humans for higher-level tasks like strategy.
Example: An e-commerce brand uses an AI agent to track competitor price changes and suggest promo copy in real time.
Human-in-the-Loop
What it is: A process where people review AI output before it goes public.
Why marketers care: Despite AI’s upside, humans are still essential for compliance, factual accuracy, and brand safety. (Plus, frankly, having “humans in the loop” is what’s keeping most of us marketers in a job.)
Example: A managing editor for a healthcare brand reviews AI-drafted FAQs to ensure they meet HIPAA guidelines before publishing.
Watermarking / Provenance
What it is: Ways to tag AI-generated content (watermarking) and track its origin (provenance).
Why marketers care: Builds transparency and helps comply with emerging disclosure rules.
Example: A news site watermarks AI-generated images and notes their origin, keeping reader trust intact.
3. Search & Discovery
RAG (Retrieval-Augmented Generation)
What it is: RAG combines a language model with a retrieval system that pulls in relevant documents from a trusted source before generating a response — so the answer reflects up-to-date, grounded info.
Why marketers care: RAG chatbots can quote the latest product specs instead of guessing, reducing fact-checking time.
Example: A customer-support bot pulls current pricing sheets on demand, cutting ticket escalations by 30%.
Semantic Search
What it is: Search that understands the context and intent behind a query to deliver more relevant results.
Why marketers care: Improves on-site search and keeps visitors engaged.
Example: A software company’s knowledge base uses semantic search so users asking, “Can I reset my login information?” will see the exact help article — even if they don’t include the word “password” in the title.
Embeddings
What it is: The vectors themselves — dense lists of numbers that represent the meaning of words or documents.
Why marketers care: High-quality embeddings drive recommendations and personalization.
Example: A media site clusters articles by topic using embeddings, then recommends related reads, boosting time on site by 15%.
Grounding
What it is: Anchoring an AI model’s answers to verified data sources to reduce errors.
Why marketers care: Prevents misinformation and protects brand authority.
Example: A marketing chatbot grounds all claims in the company’s most recent product manual before responding to customer questions.
4. Emerging Use Cases
LLMO/AISO/GEO/AIO/AI Search
What it is: A cluster of vendor-coined terms for “next-gen” AI search features. The industry has yet to land on a cohesive, widely agreed-upon term for this — but it’s essentially the next generation of search. Note: These aren’t technical terms, but they’re often vendor-specific labels for AI-powered search features.
Why marketers care: The reshaping of search is one of the biggest marketing curveballs in years. Organic traffic, once the golden goose, is getting squeezed by AI Overviews, chatbot summaries, and zero-click results. But on the plus side, marketers who figure out how to stay visible in this new landscape are about to win big.
Example: Your boss demands that your brand starts showing up in GEO. Er, wait. AIO. Or, is it LLMO? However they phrase it, they want your product or service to show up when a user searches for a relevant query on ChatGPT, Perplexity, or in Google’s AI Mode.
Synthetic Data
What it is: Artificially generated data that mimics real-world information but contains no personally identifiable details.
Why marketers care: Lets teams test personalization models without risking customer privacy.
Example: A retail brand trains a recommendation engine on synthetic shopping histories before switching to live data, catching edge-case bugs (and possible security snafus) early.
Orchestration Layer
What it is: Middleware that connects multiple AI tools and business rules into one streamlined workflow.
Why marketers care: Prevents the “tool zoo” problem by moving content from creation to CMS without manual copy-pasting.
Example: An orchestration layer routes AI-generated product descriptions through compliance review and into the e-commerce platform automatically.
Learning the language of AI can feel like trying to order off a menu where the options are written in math. But once you crack the code, it’s a lot easier to make smart choices — about your tools, your team, and your strategy.
Bookmark this glossary. Share it with your boss. Drop “semantic embeddings” into your next Slack thread. Because the marketers who speak AI fluently stand to get ahead in more ways than one.
Tired of tool overload? Juggling platforms that don’t talk to each other? Contently’s AI Studio marries strategy, editorial oversight, and performance in one AI-powered workflow.
Frequently Asked Questions (FAQ):
I’m overwhelmed. Which AI terms should I actually memorize?
Start with the basics that show up most often in tools and vendor convos: LLM, prompt engineering, hallucinations, and tokens. If you’re working on content ops or AI search strategy, add in grounding, embeddings, and vector search. You don’t need to become a machine learning expert — just fluent enough to make smart decisions (and spot hype when you hear it).
What’s the difference between AI search and traditional SEO?
Traditional SEO is about optimizing for search engines; AI search is about showing up in answers. Instead of just links, tools like ChatGPT, Google AI Overviews, and Perplexity generate responses based on content they’ve crawled or retrieved. That changes how visibility works — and means brands need to start thinking less in terms of keywords and more in terms of “answer authority.”
How do I actually start using this stuff in my content workflow?
You don’t need a PhD or a five-figure budget to make AI useful. Start small: Try using a language model to draft a blog outline, summarize a webinar, or generate SEO headlines. Then layer on more advanced workflows — like chaining prompts or fine-tuning — once you’re comfortable. Test, tinker, and build from there.
