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) – Methods that learn patterns from data and improve performance without being explicitly programmed for each outcome.
Generative AI – Models that create new content such as text, images, audio, or code rather than only analyzing existing data.
Large Language Models (LLMs) – Models trained on massive text datasets that can 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 produce explainable conclusions. Capabilities vary by model and vendor.
Diffusion Models – Generative models that create realistic images, video, or audio by gradually transforming random noise into coherent outputs.
Artificial General Intelligence (AGI) – A hypothetical level of AI that could perform any intellectual task a human can. No system today meets this standard.
Artificial Superintelligence (ASI) – A theoretical form of AI that would exceed human intelligence in all areas. ASI remains speculative.
Foundational Model Builders – The companies that design and train large-scale models used across industries, including 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 required to train and deploy 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 under human oversight and governance.
Model Context Protocol (MCP) – An open standard that enables AI agents and applications to communicate, authenticate, and securely share information across tools and environments. (modelcontextprotocol.io)
AdCP (Ad Context Protocol) – An emerging standard that defines how AI agents, advertisers, and publishers exchange information during automated ad transactions. It aims to improve speed, transparency, and auditability.
ACP (Agentic Commerce Protocol) – An emerging standard that defines how AI agents negotiate, purchase, and complete transactions under defined business rules, enabling secure, auditable machine-to-machine commerce.
ACO (Agentic Commerce Optimization) – A strategy for preparing product catalogs, feeds, checkout systems and metadata so that AI shopping agents not only discover your offerings but also transact on your behalf. Mastering ACO means shifting from human-click conversion to being selected by machines.
Connecting AI Systems
API (Application Programming Interface) – A set of rules that allows one software system to interact with another. APIs enable AI tools to access CRMs, ERPs, or other enterprise systems.
Retrieval-Augmented Generation (RAG) – A technique that retrieves trusted information at query time to improve the accuracy and reliability of AI-generated responses.
Context Window – The amount of information an AI model can process at once. Larger windows allow longer and more complex instructions.
Knowledge Graph – A structured database of entities and relationships that allows AI systems to reason, infer, and personalize results.
Vector Database – A database optimized for storing data as vector embeddings, enabling AI systems to retrieve information by meaning rather than by keyword match.
Data Fabric – An integrated data layer that connects disparate sources, ensuring AI systems can find, access, and use information consistently across an organization.
Developing and Adapting AI
Fine-Tuning – The process of retraining a pre-trained model on specialized data to improve its performance on a defined task or within a domain.
Low-Code AI – Platforms that let developers or analysts build AI workflows with minimal hand-written code.
No-Code AI – Visual tools that allow non-technical users to build AI applications through drag-and-drop interfaces. Useful for prototypes and internal tools.
Synthetic Data – Artificially generated data used to train or test models when real data is limited, sensitive, or unavailable. Helps reduce privacy risks and improve balance in datasets.
Evaluation Dataset – A controlled dataset used to measure model performance, reliability, and bias before deployment.
Prompting and Context Design
Prompt Engineering (Prompt Crafting) – Writing precise and structured instructions that guide AI systems to deliver relevant, accurate, and consistent outputs.
Pre-Prompt – The hidden or system-level instructions that shape an AI’s behavior before any user input is processed.
Meta Prompt – A higher-level directive that governs how an AI interprets and responds to all subsequent prompts in a workflow or session.
JSON Context Profile – A structured file that defines roles, tone, audience, and behavioral rules for an AI system to maintain consistent responses across sessions and agents.
Prompt Orchestration – The coordination of multiple prompts or models to complete a multi-step task with repeatability and auditability.
Context Engineering – Structuring information so AI systems understand what matters before generating outputs, improving accuracy and business alignment.
Creative and Human-Language Interfaces
Vibe Coding / Vibe Marketing / Vibe Prompts – Specifying tone, mood, or style in plain language and allowing AI to generate content that fits that description.
AEO (Answer Engine Optimization, also called Generative Engine Optimization or GEO) – Structuring digital content so AI assistants and search engines can understand, cite, and present it accurately. This includes using JSON-LD (JavaScript Object Notation for Linked Data) – a W3C standard for embedding structured, machine-readable data into web pages so AI systems can correctly interpret brand, product, and factual information.
Governance and Measurement
Guardrails (Governance Layer) – The policies, controls, and monitoring systems that govern how AI is deployed, maintained, and audited inside an organization.
AI Policy – The documented guidelines that define responsible AI use across an organization, including ethics, data protection, and compliance requirements.
Model Card – A standardized document describing a model’s purpose, training data, limitations, and performance metrics. Used for transparency and accountability.
Audit Trail – A secure, immutable record of who interacted with an AI system, when, and how. Essential for governance and compliance.
Evals – Metrics used to evaluate model performance, including accuracy, robustness, safety, latency, and cost efficiency.
Information and Research Tools
Deep Research Tools – AI applications that search, summarize, and synthesize information from multiple trusted sources. Examples include Perplexity, NotebookLM, and ChatGPT with browsing.
These Terms Will Continue to Evolve
AI terminology changes quickly. Aligning on definitions before strategic discussions improves clarity, decisions, and results.
Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it. This work was created with the assistance of various generative AI models.
Read the full article, How to Talk About AI in a Meeting by Shelly Palmer