AI Glossary
Clear, practical explanations of AI concepts—from fundamentals to advanced techniques. Built for builders.
Agentic AI
AI systems that can autonomously plan, reason, and execute multi-step tasks with minimal human intervention.
AI Agents
Autonomous AI systems that can perceive their environment, make decisions, and take actions to achieve specific goals.
AI Hallucination
When an AI model generates information that sounds plausible but is factually incorrect, fabricated, or nonsensical.
AI Orchestration
The coordination of multiple AI models, tools, and workflows to accomplish complex tasks that no single model could handle alone.
AI Safety
The field focused on ensuring AI systems behave as intended, avoid harmful outputs, and remain under human control.
API
Application Programming Interface—a set of rules that allows different software applications to communicate and share data.
Attention Mechanism
A technique that allows AI models to focus on relevant parts of input when processing, enabling better understanding of context and relationships.
Chain of Thought
A prompting technique that improves AI reasoning by asking the model to show its step-by-step thinking process.
Chatbot
A software application designed to simulate conversation with human users, ranging from simple rule-based systems to advanced AI assistants.
Context Window
The maximum amount of text (measured in tokens) that a language model can process in a single interaction.
Conversational AI
AI systems designed to engage in natural dialogue with humans, understanding context and generating relevant responses.
Few-Shot Learning
Teaching AI models to perform tasks by providing a small number of examples (typically 1-10) in the prompt.
Fine-tuning
The process of further training a pre-trained AI model on a specific dataset to improve its performance on particular tasks.
Foundation Model
Large AI models trained on broad data that can be adapted to many downstream tasks, serving as a base for specialized applications.
Function Calling
The ability of AI models to identify when a user request requires an external function and generate the structured data needed to call it.
Generative AI
AI systems that can create new content—text, images, audio, video, or code—rather than just analyzing existing data.
GPT (Generative Pre-trained Transformer)
A series of large language models by OpenAI that generate text by predicting the next word, powering ChatGPT and many AI applications.
Machine Learning
A type of artificial intelligence where systems learn patterns from data to make predictions or decisions without explicit programming.
Model Context Protocol (MCP)
An open protocol that standardizes how AI assistants connect to external data sources, tools, and systems.
Multimodal AI
AI systems that can process and generate multiple types of data—text, images, audio, and video—within a single model.
Natural Language Processing (NLP)
The field of AI focused on enabling computers to understand, interpret, and generate human language.
Neural Network
A computing system inspired by the human brain, using interconnected nodes (neurons) to learn patterns from data.
No-Code AI
Platforms and tools that enable users to build AI applications through visual interfaces without writing code.
Pre-training
The initial phase of training AI models on large datasets to learn general patterns before specializing for specific tasks.
Prompt Engineering
The practice of designing and refining inputs to AI models to get better, more accurate, and more useful outputs.
Prompt Injection
A security vulnerability where malicious inputs manipulate AI systems into ignoring their instructions or performing unintended actions.
Semantic Search
Search that understands meaning and intent rather than just matching keywords, using AI to find conceptually similar content.
System Prompt
Special instructions given to an AI model that define its behavior, personality, and constraints before any user interaction.
Temperature
A parameter that controls the randomness and creativity of AI model outputs, with lower values being more deterministic.
Tokens
The basic units that language models use to process text, typically representing parts of words, whole words, or punctuation.
Transformer
The neural network architecture that powers most modern AI language models, using attention mechanisms to process sequences efficiently.
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