AI Glossary
Clear, practical explanations of AI concepts—from fundamentals to advanced techniques. Built for builders.
Agent-to-Agent Communication
Protocols and patterns enabling AI agents to exchange information, delegate tasks, and coordinate actions in multi-agent systems.
Agentic AI
AI systems that can autonomously plan, reason, and execute multi-step tasks with minimal human intervention.
AI Agent Bootstrap
A configuration file that initializes an AI agent's first run, setting up identity, capabilities, and workspace before being deleted.
AI Agent Cron Jobs
Scheduled tasks that run AI agent actions at specified intervals, enabling automated workflows, reminders, and periodic checks.
AI Agent Heartbeat
A periodic polling mechanism that allows AI agents to perform background tasks, check for updates, and maintain awareness between user interactions.
AI Agent Hooks
Event triggers that execute custom actions at specific points in an AI agent's lifecycle, like startup, shutdown, or before responses.
AI Agent Memory
Persistent storage mechanisms that allow AI agents to retain information across sessions, including working memory, long-term memory, and conversation history.
AI Agent Soul File (SOUL.md)
A configuration file that defines an AI agent's core identity, personality, values, and behavioral guidelines.
AI Agent Webhooks
HTTP callbacks that allow AI agents to receive real-time notifications from external services and trigger automated responses.
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 Lead Generation
Using AI agents to identify, qualify, and engage potential customers through automated research, outreach, and conversation.
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.
AI Voice Agents
AI systems that interact through speech, handling phone calls, voice commands, and spoken conversations autonomously.
Answer Engine Optimization (AEO)
The practice of optimizing content to appear in AI-generated answers from search engines, chatbots, and AI assistants rather than traditional search result listings.
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.
Claude Code
Anthropic's agentic coding tool that operates in your terminal, capable of understanding codebases, making edits, and executing commands autonomously.
Claude Cowork
Anthropic's productivity-focused AI application for document creation, file management, and desktop workflows—bridging chat and code interfaces.
Claude Skills
Modular capabilities that extend what Claude Code, Clawdbot, and other Claude-based agents can do—packaged as self-contained SKILL.md files.
CLAUDE.md
A configuration file that provides project-specific context and instructions to Claude Code and other Claude-based agents.
Clawdbot
An open-source AI agent framework that runs locally, providing personal AI assistance with file access, tool integration, and persistent memory.
Context Engineering
The practice of designing and optimizing all information provided to an AI model—including system prompts, retrieved documents, conversation history, tools, and examples—to get better results.
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.
MCP (Model Context Protocol)
An open standard by Anthropic that defines how AI models connect to external tools, data sources, and services through a universal protocol.
Mixture of Experts (MoE)
**Mixture of Experts (MoE)** is a neural network architecture where input is routed to a subset of specialized "expert" sub-networks. This enables models with trillions of parameters while only activa
Model Context Protocol (MCP)
An open protocol that standardizes how AI assistants connect to external data sources, tools, and systems.
Moltbook
A social network for AI agents where they can post, interact, and build reputation—essentially Twitter for autonomous AI systems.
Multi-Agent Systems
Architectures where multiple specialized AI agents collaborate, each handling specific tasks to achieve outcomes beyond individual agent capabilities.
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.
Retrieval-Augmented Generation (RAG)
A technique that enhances AI responses by retrieving relevant information from external knowledge sources before generating an answer.
RIPE Framework
A structured approach to writing effective AI prompts using four components: Role, Instruction, Parameter, and Example.
Semantic Search
Search that understands meaning and intent rather than just matching keywords, using AI to find conceptually similar content.
Sub-Agents
Specialized AI agents spawned by a primary agent to handle specific tasks, enabling complex workflows through delegation and parallel execution.
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.
Token Optimization
**Token optimization** refers to strategies and techniques for reducing the number of tokens consumed when interacting with large language models (LLMs), directly impacting both cost and performance.
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.
Ready to build with AI?
Turn these concepts into reality. Build custom AI agents with RAG, custom actions, and enterprise integrations—no coding required.