AI & Automation Glossary

A2A Protocol

An open standard introduced by Google in April 2025 that enables AI agents to discover, communicate with, and delegate tasks to each other autonomously across different platforms and vendors.

AI Agent

An AI system that can autonomously plan and execute multi-step tasks by using tools, making decisions, and taking actions in a sequence — going beyond single-turn question-and-answer to complete complex workflows with minimal human intervention.

AI Content Generation

The use of large language models (LLMs) and related AI technologies to automatically produce written content such as blog articles, product descriptions, social media posts, and SEO copy.

Large Language Model

A type of artificial intelligence system trained on massive amounts of text data to understand and generate human language — the technology underlying tools like ChatGPT, Claude, and Gemini that powers modern AI writing, analysis, and content tools.

MCP (Model Context Protocol)

An open standard developed by Anthropic that defines how AI applications connect to external tools, data sources, and services — enabling AI models to access real-time information, execute actions, and integrate with existing software systems in a standardized way.

Prompt Engineering

The practice of designing and refining the instructions given to AI language models to achieve specific, accurate, and useful outputs — encompassing techniques like few-shot examples, chain-of-thought instructions, role assignment, and output format specification.

RAG (Retrieval-Augmented Generation)

An AI architecture that enhances Large Language Model outputs by retrieving relevant information from an external knowledge base before generating a response — combining the language fluency of LLMs with the accuracy of targeted document retrieval.