AI Glossary: 27 Terms That Actually Matter
Plain-English definitions for busy professionals. No jargon.
| Term | What It Means (Plain English) |
|---|---|
| AI (Artificial Intelligence) | Computer systems that mimic human intelligence — learning from data, recognizing patterns, and solving problems. |
| Generative AI (Gen AI) | AI that creates new content — text, images, audio, video — rather than just analyzing existing data. |
| AGI (Artificial General Intelligence) | Hypothetical AI that can perform any intellectual task a human can. Does not exist yet — current AI excels at specific tasks, not general reasoning. |
| LLM (Large Language Model) | The AI brain behind tools like ChatGPT and Claude. Trained on massive text data to understand and generate human language. |
| Multimodal | AI that handles text, images, audio, and video — not just text. Most frontier models are now multimodal. |
| GPT | Generative Pre-trained Transformer. OpenAI's family of models powering ChatGPT. |
| Claude | Anthropic's AI model. Known for longer context handling and nuanced reasoning. |
| Gemini | Google's AI model. Integrated across Google Workspace and Search. |
| Tokens | The currency of AI — how it measures and charges for text. Roughly 1 token = 0.75 words. Limits how much you can input and receive. |
| Context Window | How much text the AI can "see" at once. Every model has a context window limit. Larger windows allow longer documents and conversations. |
| Temperature | A setting that controls how creative or predictable AI responses are. Low temperature = focused and consistent. High temperature = more varied and creative. |
| Prompt | The instruction or question you give to an AI. Better prompts get better outputs. |
| Prompt Engineering | The skill of crafting prompts that get useful, accurate, and relevant outputs from AI. |
| Context Engineering | Designing what information AI receives, in what order, to improve reasoning quality. The evolution beyond prompt engineering. |
| System Prompt | Hidden instructions that shape how an AI behaves. Custom GPTs and enterprise setups use these. |
| Custom GPT | A personalized ChatGPT you configure for specific tasks or knowledge. No coding required. |
| Hallucination | When AI confidently generates false or made-up information. Always verify critical facts. |
| Grounding | Connecting AI to real, verified information sources (documents, databases, search) to reduce hallucinations and improve accuracy. |
| RAG (Retrieval-Augmented Generation) | A grounding technique where AI retrieves from external documents or databases before generating a response. |
| Vector Database | A database that stores information as mathematical representations (vectors), enabling AI to search by meaning rather than keywords. Powers RAG and semantic search. |
| API (Application Programming Interface) | A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own tools. |
| Automation Platforms | Tools that connect apps and automate workflows — often enhanced with AI. Examples: Zapier, Make, n8n. |
| AI Automation | Using AI to automate repetitive tasks and workflows — from email sorting to report generation to data entry. |
| Agentic AI | AI designed to independently achieve complex goals with minimal human intervention. Can reason, plan, and use software tools to execute multi-step workflows on its own. |
| MCP (Model Context Protocol) | An emerging standard that allows AI agents to connect with external tools and data sources in a consistent way. |
| AEO (Answer Engine Optimization) | The practice of structuring content so AI-powered search engines — like ChatGPT, Gemini, and Perplexity — select it as the direct answer to a user's query. The successor to traditional SEO in an AI-first search world. |
| GEO (Generative Engine Optimization) | The practice of structuring digital content and managing online presence to improve visibility in responses generated by generative AI systems. It influences how large language models retrieve, summarize, and present information about your brand. |
AI (Artificial Intelligence)
Computer systems that mimic human intelligence — learning from data, recognizing patterns, and solving problems.
Generative AI (Gen AI)
AI that creates new content — text, images, audio, video — rather than just analyzing existing data.
AGI (Artificial General Intelligence)
Hypothetical AI that can perform any intellectual task a human can. Does not exist yet — current AI excels at specific tasks, not general reasoning.
LLM (Large Language Model)
The AI brain behind tools like ChatGPT and Claude. Trained on massive text data to understand and generate human language.
Multimodal
AI that handles text, images, audio, and video — not just text. Most frontier models are now multimodal.
GPT
Generative Pre-trained Transformer. OpenAI's family of models powering ChatGPT.
Claude
Anthropic's AI model. Known for longer context handling and nuanced reasoning.
Gemini
Google's AI model. Integrated across Google Workspace and Search.
Tokens
The currency of AI — how it measures and charges for text. Roughly 1 token = 0.75 words. Limits how much you can input and receive.
Context Window
How much text the AI can "see" at once. Every model has a context window limit. Larger windows allow longer documents and conversations.
Temperature
A setting that controls how creative or predictable AI responses are. Low temperature = focused and consistent. High temperature = more varied and creative.
Prompt
The instruction or question you give to an AI. Better prompts get better outputs.
Prompt Engineering
The skill of crafting prompts that get useful, accurate, and relevant outputs from AI.
Context Engineering
Designing what information AI receives, in what order, to improve reasoning quality. The evolution beyond prompt engineering.
System Prompt
Hidden instructions that shape how an AI behaves. Custom GPTs and enterprise setups use these.
Custom GPT
A personalized ChatGPT you configure for specific tasks or knowledge. No coding required.
Hallucination
When AI confidently generates false or made-up information. Always verify critical facts.
Grounding
Connecting AI to real, verified information sources (documents, databases, search) to reduce hallucinations and improve accuracy.
RAG (Retrieval-Augmented Generation)
A grounding technique where AI retrieves from external documents or databases before generating a response.
Vector Database
A database that stores information as mathematical representations (vectors), enabling AI to search by meaning rather than keywords. Powers RAG and semantic search.
API (Application Programming Interface)
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own tools.
Automation Platforms
Tools that connect apps and automate workflows — often enhanced with AI. Examples: Zapier, Make, n8n.
AI Automation
Using AI to automate repetitive tasks and workflows — from email sorting to report generation to data entry.
Agentic AI
AI designed to independently achieve complex goals with minimal human intervention. Can reason, plan, and use software tools to execute multi-step workflows on its own.
MCP (Model Context Protocol)
An emerging standard that allows AI agents to connect with external tools and data sources in a consistent way.
AEO (Answer Engine Optimization)
The practice of structuring content so AI-powered search engines — like ChatGPT, Gemini, and Perplexity — select it as the direct answer to a user's query. The successor to traditional SEO in an AI-first search world.
GEO (Generative Engine Optimization)
The practice of structuring digital content and managing online presence to improve visibility in responses generated by generative AI systems. It influences how large language models retrieve, summarize, and present information about your brand.
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