This A–Z glossary breaks down the most essential Generative AI terms for marketing and communications teams, from foundational concepts to advanced techniques. It’s designed to help professionals navigate the evolving AI landscape with confidence, clarity, and strategic insight.
If you’ve heard someone talk about agents, RAG, or temperature settings and thought, “what in the world are they talking about?” This glossary is for you!
We’ve rounded up the most common terms used in Generative AI (Gen AI), tailored for marketing and communications professionals. Whether you’re just getting started or brushing up for your next strategy session, this A–Z guide will help you make sense of the Gen AI lingo so you can participate confidently, experiment intelligently, and lead strategically.
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Artificial General Intelligence (AGI): An AI system with human-level intelligence that can learn and perform any task across domains without needing to be trained on those tasks beforehand. Unlike today’s AI, which is narrow and task-specific, AGI is general-purpose, fully autonomous, and capable of learning in real time from experience. It does not yet exist.
AI Agent: A digital worker powered by AI that can execute tasks with varying autonomy (from assisted to autonomous) based on instructions, goals, or context (e.g., summarize a meeting, draft an email, pull campaign performance data). In marketing/comms, agents are increasingly paired with guardrails and approvals.
Agentic AI: The broader paradigm where AI systems can plan, decide, and act across multiple steps and tools (e.g., draft → review against brand rules → generate variants → schedule → report outcomes).
AI Governance: The policies, controls, and review processes that ensure AI use aligns with brand, legal, privacy, and ethical standards (especially important in comms-heavy orgs and regulated industries).
Alignment: The process of ensuring an AI system behaves in accordance with set of values and intentions. Especially relevant when tuning AI outputs to brand tone or ethical guidelines.
Anthropic: The company behind Claude, a major competitor to OpenAI and Google in developing safe, steerable AI models.
Bias: The tendency of a model to systematically favor or disadvantage certain outcomes or groups, often arising from skewed data samples, feature selection, or modeling choices. Recognizing and mitigating bias is essential to ensure fair, reliable, and trustworthy AI systems.
BLEU Score: A metric used to evaluate how closely AI-generated text matches human-written content. Often used in benchmarking.
Brand Fine-Tuning: Fine-tuning or adapting a model using brand-specific examples (voice, terminology, claims boundaries) so outputs consistently sound like you and stay within approved language.
Chain of Thought (CoT): A prompting technique that asks AI to lay out each reasoning step and to “thinking out loud”. This mimics the way human’s think and the results are more logical, on-point responses for things like mapping out campaign strategies or drafting PR plans.
Chain of …… Strategies: Variants of Chain of Thought prompting patterns, such as chain of density, chain of verification, used to drive deeper or more specific reasoning pathways in AI outputs. These strategies help marketers explore scenario planning, messaging variations, or editorial critique flows within content drafts. Helpful for editorial refinement, objection handling, and PR response planning.
Claude: A family of AI models by Anthropic, known for long context windows and conversational tone.
Content Credentials: Metadata that helps signal whether content was edited or generated, increasingly relevant as audiences, platforms, and regulators push for authenticity and provenance.
Context Window: The maximum amount of text (in tokens) an AI model can keep in “working memory” at once. A larger window helps track campaign context, audience insights, brand rules, and multi-doc briefs in one session.
Copilot: An AI-powered assistant, most notably Microsoft 365 Copilot, that integrates with tools like Word, Teams, Excel, PowerPoint, Outlook, and Dynamics. Copilot can draft emails, summarize meetings, generate campaign ideas, analyze data, and create visual assets.
Custom GPT: A version of ChatGPT customized with specific behavior, instructions, and knowledge for particular use cases. This ensures, for example, that ChatGPT consistently produces on-brand marketing copy, social posts, or press materials. Often paired with a review workflow.
Diffusion Model: An image-generation method that begins with random noise and then refines it in small steps until a clear picture emerges, letting you create custom, on-brand visuals with tools like DALL·E or Midjourney.
Domain Adaptation: Retraining or fine-tuning a general AI model with examples from your own industry, audience, or brand voice so it generates domain-accurate, audience-relevant outputs.
Data Provenance: Tracking where a claim, dataset, or retrieved excerpt came from. In comms, provenance supports credibility, approvals, and auditability.
Embedding: Representation of words, phrases, or documents in a way that captures their meaning and relationships in AI models. They are used to power search for brand assets, group similar audience segments, and retrieve on-brand content or customer insights quickly.
EM Dash: A long dash used to create emphasis, insert interruptions, or set off clauses. While common in editorial writing, the em dash has become a telltale sign of AI-generated content, especially from tools like ChatGPT, which often overuse it to mimic human rhythm or tone. 'Hotly' debated, as seen on LinkedIn, by folks who see this as an AI-generated tell and the 'for' side arguing that it is grammatically correct to use it in sentences. Maybe the problem is just bad writing. We have developed our own framework to help you enhance output quality. Get our guide here: The Generative AI Prompt Engineering Guide
Explainability: The degree to which an AI’s decisions and outputs can be understood and interpreted by humans.
Fine-Tuning: Adjusting a base AI model using specific datasets to improve performance on domain-specific tasks. For marketing/comms, this often means improved voice consistency, terminology, and approved phrasing. It does require strong governance.
Foundation Model: A large, pre-trained model, GPT-class or Claude-class, that serves as a base for various applications.
Generative AI: A class of AI that creates new content, such as text, images, video, or code, based on input prompts.
GEO (Generative Engine Optimization): AKA Answer Engine Optimization (AEO), Large Language Model Optimization (LLMO) or Artificial Intelligence Optimization (AIO) is the practice of optimizing content to rank well in AI-generated answers from tools like ChatGPT, Perplexity , or Google’s Search Generative Experiences (SGE), rather than traditional search engines. Focuses on structuring content for clarity, authority, and AI relevance, instead of keyword density.
Gemini: Google’s family of advanced AI models designed to understand and generate text, images, audio, and code. Known for its strong multimodal performance, Gemini is integrated across Google Workspace products. For marketers and comms teams, it enables everything from campaign copywriting and asset generation to audience insights and trend monitoring, especially within Google-native environments.
Grounding: Connecting AI outputs to trusted sources (brand docs, approved FAQs, product sheets, credible references) to reduce hallucination and improve factual accuracy.
GPT (Generative Pre-trained Transformer): A specific type of large language model architecture. It is designed to predict and generate human-like text based on a prompt. GPT models power tools like ChatGPT and can be customized to reflect your brand voice, content style, or audience needs.
Grok: An AI assistant built by xAI (Elon Musk’s AI company) and integrated into X (formerly Twitter). Known for its sarcastic tone and access to real-time X data. Often discussed in comms contexts because of its relationship to real-time social discourse.
Hallucination: When AI passes off information as factual when in fact, it is not. Gen AI models have a tendency to make up information. A key issue in marketing and brand-sensitive workflows.
Human-in-the-Loop (HITL): A workflow where humans review, approve, or correct AI outputs before publishing or acting. It is especially important for marketing and comms teams to adopt as a standard for brand-sensitive content, PR, and regulated messaging.
Instruction Tuning: A training process that teaches AI models to better understand and follow human instructions, making them more responsive and useful for tasks like drafting content, summarizing reports, or answering brand-specific queries.
Inference: The moment when an AI model takes your input, like a prompt or question, and generates a response.
Intent Modeling: Using AI to infer what the audience is actually trying to accomplish, and then improving content strategy, GEO structure, and personalization.
Just-In-Time Learning: An emerging technique where AI models fetch and learn from relevant data in real-time, rather than during initial training, to adapt to the latest brand updates or news. Useful when you need content that reflects latest campaign performance or shifting messaging context.
Knowledge Cutoff: The latest date of information that a model was trained on. Anything after that may be unknown to it unless it has web access.
Knowledge Graph: A structured map of entities, from brand, product, leaders, issues, competitors, and their relationships. Supports more consistent AI outputs and stronger grounding.
Large Language Model (LLM): A category of AI models trained on massive amounts of text to understand and generate natural language. LLMs can perform a wide range of language tasks, like drafting content, summarizing reports, or answering questions, and serve as the foundation for tools like ChatGPT or Claude.
Latency: The time it takes for an AI model to respond. Lower latency means faster results.
LLMS.txt: A lightweight text file (usually Markdown) placed at your website’s root to tell AI models which pages or sections to prioritize. Think of it as a curated map for LLMs. It helps tools like ChatGPT or Google (in theory) better understand your high-value content. While discussed heavily, adoption and respect across providers is inconsistent and evolving, so treat it as experimental / future proofing.
Manus: An autonomous AI agent introduced in early 2025 by the startup Monica. Manus can interpret high-level prompts, plan multistep workflows, and execute tasks independently, like compiling reports or publishing dashboards.
Model Card: A document that outlines an AI model’s capabilities, limitations, training data, and intended use cases. Useful for marketing and comms teams to assess whether a model is safe, reliable, and appropriate for brand or audience-specific applications.
Multimodal: Refers to an AI model that can process and combine different types of data, like text, images, audio, or video, in a single interaction. For example, a multimodal model could analyze an image and generate a caption, or summarize a video transcript, enabling richer content creation and campaign insights.
Natural Language Processing (NLP): The field of AI that focuses on understanding and generating human language.
Narrative Intelligence: AI-assisted analysis of how stories and frames evolve across media and social platforms. This is useful for comms strategy and reputation monitoring.
Non-Determinism: The idea that the same prompt might result in different outputs due to randomness in generation.
OpenAI: The organization behind ChatGPT, one of the leading companies in Gen AI development.
Open-Source Model: A model whose architecture and weights are publicly accessible and modifiable (e.g., Mistral, Llama).
Ontology: Your internal taxonomy for how topics, products, messages, and entities are categorized. This helps AI systems stay consistent and improves retrieval accuracy.
Perplexity: An AI-powered answer engine and search tool that fetches real-time information from the web and generates conversational answers with inline citations. Brands are starting to optimize for Perplexity to ensure they get properly cited in AI-generated responses. It’s rapidly becoming a reference point in AI SEO and brand visibility.
Prompt Engineering: The craft of designing prompts to achieve the desired AI output. A skillset especially relevant for marketing, copywriting, and strategy teams. Click here for our free prompt engineering guide.
Prompt Library: A shared internal repository of tested approved prompts (and examples) aligned to use cases like campaign briefs, exec comms, Q&A prep, and crisis statements. Learn more about tailored workshops, where you will learn how to build your team's prompt library. AI Certification with Sequencr: Training and Enablement
Prompt Injection: A security concern where an attacker manipulates a model’s behavior by embedding malicious text within the prompt. High risk when AI can access internal docs, drive actions, or publish content.
Pre-training: The initial phase where a model learns from massive public datasets before any customization.
Post-Generation Editing: A structured review layer to humanize tone, verify facts, confirm claims boundaries, and align with brand voice before publishing.
Quantization: A technique used to make AI models smaller and faster by simplifying the numbers they use, reducing file size and response time with minimal impact on output quality. It helps teams run AI tools more efficiently, especially on limited hardware or in real-time content workflows.
Query Fan-Out: An AI technique where a single prompt is split and sent to multiple sources, like different models, APIs, or tools, in parallel. The system then combines the responses into one unified answer. This improves accuracy, coverage, or speed when generating content, insights, or reports across multiple data sources. Query Fan out is common in Gen AI search.
Quality Thresholding: Automatically filtering AI outputs based on standards like factuality, tone match, and claim safety.
Reasoning Models: AI models designed to break down complex tasks step-by-step, explain their logic, and generate more structured, accurate outputs. Ideal for tasks that require analysis, planning, or multi-step thinking, like strategic briefs or performance insights. We've covered a topic about reasoning models in an article. Read more here.
Retrieval-Augmented Generation (RAG): A technique that combines AI generation with real-time search. The model retrieves relevant documents or data from trusted sources or a knowledge base, like brand guidelines or product FAQs, before generating a response. This improves accuracy and traceability: to ensure outputs reflect up-to-date, brand-specific information.
Reputation Monitoring 2.0: AI-driven reputation intelligence that analyzes coverage, sentiment, and narratives across media/social platforms. Increasingly also considering how brands appear within generative answers.
Small-Language Model (SLM): A lightweight version of a large language model (LLM), designed for specific tasks, faster responses, lower cost, or use with limited data. Often easier to deploy and fine-tune for targeted use cases like brand-specific content or customer service.
Shadow AI: Use of unapproved AI tools by employees, increasing privacy, IP, and compliance risk.
Synthetic Data: AI-generated data used for training or simulation. It can improve model performance when real data is scarce but comes with risks and downsides.
Style Transfer: Applying the style of one text or image to another using AI. It’s helpful in brand voice and creative direction.
System Prompt: The hidden instructions that guide how an AI model behaves behind the scenes. It shapes tone, format, and priorities, like staying concise or following brand guidelines. Critical when customizing GPTs for consistent output.
Temperature: A setting that controls randomness in AI outputs. Higher values (e.g., 1.0) yield more creative results, lower values (e.g., 0.2) yield more predictable ones.
Token: A chunk of text (word or sub-word) processed by an AI model. Prompt length is measured in tokens.
Traceability: The ability to track what sources or rules influenced an output. This is increasingly important for enterprise approvals and regulated comms.
Use Case Library: A catalog of tasks where AI can be applied effectively. It is often customized by function (e.g., marketing, sales, ops).
User Persona: A fictional representation of the target audience used to tailor AI prompts and outputs for tone, language, and context.
Voice Cloning: The AI-generated recreation of a person’s voice. Increasingly used in content production, with important ethical considerations.
Vision-Language Models (VLMs): Models that connect image and text understanding (e.g., analyzing a visual and generating structured insights), useful for asset management and creative review.
Weights: The parameters a model learns during training that determine how it makes predictions or generates responses.
Web-Connected AI: An AI model that can pull live information from the web, increasing its awareness of real-time events and updates. An example of this is Perplexity.
Workflow Automation Layer: Orchestration across tools from CMS, CRM, analytics, DAM to calendars, so AI can support end-to-end execution, not just drafting.
xAI: Elon Musk's AI company developing models and tools to compete in the large-language-model space, with an emphasis on transparency, safety, and open-science principles. Potentially relevant for brand comms, especially when comparing provider positioning or AI ethics strategies.
Yield: A metric that measures how often AI-generated output meets a desired threshold of quality or brand alignment. For marketing teams, monitoring yield helps you track the percentage of prompt responses that require minimal editing or brand adjustment.
Zero-Shot Learning: When an AI performs a task it hasn’t been specifically trained on, using only general knowledge.
Zoom Fatigue Copilot: A humorous nod to Gen AI tools that summarize, transcribe, or action meetings, which are emerging as go-to's for overloaded teams.
