AI isn’t hard to understand. It’s hard to talk about clearly. The technology moves faster than the language used to describe it, which leads to misalignment and wasted time. Every effective AI meeting starts with a shared vocabulary.

Use this glossary to make sure your team is literally on the same page before making decisions that shape strategy, governance, and execution. You can download a printable PDF here.

AI Glossary for Today’s Meeting

Core Concepts

Artificial Intelligence (AI) – Computer systems that perform tasks that typically require human intelligence, such as understanding language, recognizing patterns, making predictions, or solving problems.

Machine Learning (ML) – Algorithms that learn from data to recognize patterns and improve their performance without being explicitly programmed.

Generative AI – AI models that create new content such as text, images, audio, or code rather than simply analyzing existing data.

Large Language Models (LLMs) – AI models trained on massive text datasets that understand and generate human-like language. Most chatbots and text-based assistants use LLMs.

Reasoning Engines – AI systems designed to analyze information, apply logic, and reach conclusions that can be explained.

Diffusion Models – AI systems that create realistic images, video, or audio by turning random noise into a finished product through many small refinements.

Artificial General Intelligence (AGI) – A hypothetical level of AI that can perform any intellectual task a human can. There is no agreed-upon definition, and no system today meets this standard.

Artificial Superintelligence (ASI) – A theoretical form of AI that would surpass human intelligence in all areas. ASI is currently considered science fiction.

Foundational Model Builders – The companies that design and train the large-scale AI models used across the industry, such as OpenAI, Google DeepMind, Anthropic, Mistral, and Meta.

Hyperscalers – Global cloud infrastructure providers like Amazon Web Services, Microsoft Azure, and Google Cloud that supply the compute power needed to train and run advanced AI systems.

Agents and Agentic Systems

Agents – Independent AI programs that perform specific tasks, such as summarizing emails, generating reports, or scheduling meetings.

Agentic Systems – Connected groups of AI agents that collaborate to complete multi-step workflows with human oversight.

Management Control Protocol (MCP) – A standard that allows AI agents and applications to communicate, authenticate, and share information securely.

AdCP (Ad Control Protocol) – A standard that defines how AI agents, advertisers, and publishers exchange information during automated ad transactions. It supports faster, transparent, and auditable ad buying.

ACP (Agentic Commerce Protocol) – A standard that lets AI agents negotiate, purchase, and complete transactions within defined business rules. It creates the framework for secure, auditable machine-to-machine commerce.

Connecting AI Systems

API (Application Programming Interface) – A set of software rules that lets one system talk to another. APIs make it possible for AI tools to pull data from CRMs, ERPs, or other enterprise systems.

Retrieval-Augmented Generation (RAG) – A technique that lets AI systems look up trusted information before answering, improving accuracy and reducing hallucinations.

Context Window – The amount of information an AI model can consider at one time. A larger window means the model can handle longer or more complex prompts.

Knowledge Graph – A structured database that links facts and relationships so AI systems can reason over them. Often used to improve retrieval, accuracy, and personalization.

Vector Database – A database optimized for storing and retrieving data as numerical representations. It enables AI systems to find information by meaning rather than by exact match.

Data Fabric – An integrated layer of data and tools that connects disparate sources, enabling AI systems to find and use information consistently across the enterprise.

Developing and Adapting AI

Fine-Tuning – Customizing a pre-trained model with specific company data to improve accuracy for a defined task or domain.

Low-Code AI – Development platforms that let users build AI workflows with minimal programming. Useful for technical teams that need to move quickly.

No-Code AI – Visual tools that let anyone build AI applications through drag-and-drop interfaces. Ideal for prototypes and internal tools.

Synthetic Data – Artificially generated data used to train or test AI models when real data is limited or sensitive. Helps reduce privacy risks and improve model performance.

Evaluation Data Set – A controlled data sample used to measure model accuracy, reliability, and bias before deployment.

Prompting and Context Design

Prompt Engineering (aka Prompt Crafting) – The craft of writing precise and structured inputs that guide AI systems to deliver relevant, high-quality responses.

Pre-Prompt – The hidden or system-level instructions that shape how an AI responds before any user input is processed.

Meta Prompt – A higher-level instruction that defines how an AI should interpret and respond to all other prompts within a session or workflow.

JSON Context Profile – A structured data file that defines roles, tone, audience, and behavioral rules for an AI system to maintain consistent responses across sessions.

Prompt Orchestration – Coordinating multiple prompts or AI tools to complete a multi-step task. Used in enterprise systems to make outputs repeatable and auditable.

Context Engineering – Structuring information so AI systems understand what matters before generating a response. The goal is relevance, accuracy, and alignment with business intent.

Creative and Human-Language Interfaces

Vibe Coding / Vibe Marketing / Vibe Anything – Giving an AI a desired tone, mood, or style in plain language and letting it create content that matches it.

AEO (Answer Engine Optimization, aka Generative Engine Optimization or GEO) – Structuring content so AI assistants and search engines can understand, cite, and present it accurately in AI-generated answers. This includes using JSON-LD (JavaScript Object Notation for Linked Data) – a standard format for embedding structured, machine-readable information into websites so AI systems can understand brand facts, products, and context.

Governance and Measurement

Guardrails (Governance Layer) – The rules, controls, and processes that manage how AI systems are deployed, monitored, and audited inside an organization.

AI Policy – The documented set of guidelines that govern how AI is used responsibly across an organization, including ethics, compliance, and data protection.

Model Card – A standardized summary that documents a model’s purpose, training data, limitations, and performance metrics. Used for transparency and accountability.

Audit Trail – A secure record of who interacted with an AI system, when, and how. Required for compliance and governance.

Evals – Measurements used to evaluate how well an AI model performs, including accuracy, reliability, and cost.

Information and Research Tools

Deep Research Tools – AI tools that search, summarize, and synthesize information from multiple sources. Examples include Perplexity, NotebookLM, and ChatGPT with browsing.

These Terms Will Continue to Evolve

These terms will continue to evolve, just as AI itself does. While we may not yet have an AI equivalent of “Google it,” taking a moment to align on definitions in meetings will ensure clarity, better decisions, and stronger business outcomes.

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