AI Glossary of Terms
AI Terms & Meanings
AI terms and concepts can feel overwhelming, especially with how fast the field moves.
Here’s a compilation of key terms and concepts to help you navigate and get started.
Bookmark it for quick reference when you encounter unfamiliar language.
Core AI Concepts
Artificial Intelligence (AI): AI is software that can do tasks that normally need human intelligence, such as understanding language, recognizing images, making decisions, or creating new things.
Agent: AI that can take actions on its own, not just answer questions. It can use tools and takes action to get things done for you, like searching the web, booking a flight, filling out forms, or running code.
Knowledge cutoff date: The last date of the data the AI was trained on. After this date it has no built-in knowledge, unless it uses tools to search the web in real time and add the fresh info to its context.
Large Language Model (LLM): Giant AI trained on enormous amounts of text (like books, websites, and conversations from the internet) so it can chat, write, and answer questions.
Machine Learning (ML): Software that lets AI learn from examples and patterns instead of following rules made by programmers.
Model: Specific AI “brain”. The same company can release new versions (e.g., GPT-4 → GPT-4o) that are smarter or faster. Some examples are GPT-4o, Claude 3.5 Sonnet, or Gemini 1.5.
Neural network: The layered “digital brain” made of layers of connected digital “neurons” that pass information to each other.
Training data: Training data is all the information (text, images, examples) an AI learns from. The quality and quantity of training data heavily decide how smart the AI becomes.
Working with AI
Command: Specific instruction that triggers a particular behavior in the AI, often starting with a slash (/). Examples include /clear and /summarize.
Context window: Total token limit for one session. It translates to how it can “see” and remember at one time in a conversation.
Effort: How much “thinking” the AI does before answering. Low effort is usually faster & cheaper, whereas high effort tends to be slower but smarter.
Human-in-the-Loop: Keeping a human person involved to review, check, or approve AI decisions.
Project: A workspace in AI tools that keeps shared instructions, files, and knowledge across many conversations.
Prompt: The instruction or question you give to an AI. The better your order, the better the result.
Prompt engineering: The skill of writing better prompts to get better, more useful answers from AI.
Session: One continuous conversation with an AI. When it ends, the AI usually forgets everything (unless it has memory tools). Session length is limited by the context window.
Skill: Predefined tool or ability that an AI agent can use to get things done. Searching the web, reading or creating files, and sending emails are some skill examples.
System prompt: Hidden instructions that tell the AI how to behave in a specific app or tool.
Token: The small pieces of text that AI uses to measure and process language (roughly 3/4 of a word). Input tokens is what you send to the AI. Output tokens is what the AI sends back. Both cost money, but output usually costs more.
Usage: How much of an AI service you’ve used. It is usually measured in tokens or API calls.
AI Behavior & Output
Bias: When AI reflects unfair patterns. This happens because AI learns from huge amounts of internet text, images, and human history. If that data has unfair patterns (like stereotypes), the AI can copy or magnify them.
Fine-tuning: Retraining an existing AI model on your own specific data so it becomes better at a particular task.
Grounding: Connecting the AI’s output to real, verifiable sources or facts to make it more trustworthy.
Hallucination: When an AI confidently makes up something that’s false. It happens because the AI doesn’t “know” things like humans. It just predicts what words should come next.
Inference: The moment when the AI generates a response (as opposed to training). Is when you use the trained AI and it creates a new answer for your specific case.
Output: Is whatever the AI produces. It can be in the form of text, images, code, audio, or anything else.
Retrieval-Augmented Generation (RAG): AI that first searches a knowledge base (documents, database) before answering, then generates a response.
Tools & Ecosystem
AI Automation: Automation that uses AI in one or multiple steps so it can handle messy, changing, or unclear situations, instead of just strict rules.
Application Programming Interface (API): Set of rules and protocols that lets developers connect AI to other apps and software.
Command Line Interface (CLI): Text-only way to control a computer or tool by typing commands, instead of clicking buttons.
Editor: A lightweight app for writing and editing text or code.
Integrated Development Environment (IDE): Comprehensive software development app to write, debug, test, and compile code. Examples include IntelliJ and Visual Studio.
JavaScript Object Notation (JSON): A structured way to organize data using curly braces {} and key-value pairs. Its used in API responses or when configuring CustomGPTs and agents.
Markdown (MD): Language to format plain text without complicated code, using simple symbols like #, **, and -. It’s become popular because of how easy for both humans and machines to read.
Model Context Protocol (MCP): Standard way for AI agents to safely connect to external tools, files, and data sources. It makes it easier and safer for agents to use real-world tools without custom coding every time.
Repository (Repo): A folder that stores all files for a project plus its version history (usually on GitHub).
SSH: Protocol that lets you safely connect to another remote computer or server over the internet.
Terminal: App with a text-based interface that lets you directly interact with your computer by typing commands.
Advanced AI Terms
AI Governance: Policies and rules that organizations create to use AI safely and responsibly.
Chunking: Splitting big documents into smaller pieces so they fit in the context window or can be searched better.
Guardrails: Safety rules that stop the AI from doing harmful, illegal, or off-topic things.
Orchestrator: Smart AI “manager” that coordinates multiple tools, agents, or steps to finish big projects or workflows.
Swarm: A group of AI agents that work together at the same time, like a team of bees.
Temperature: A setting that controls how creative (or how safe/predictable) the AI’s answers are.
Vectors / Embeddings: A way to turn text into numbers so AI can understand meaning and find similar content.
