19 terms defined

AI Glossary 2026

Plain-English definitions of every AI term you need to know — from AI agents and RAG to MCP, vibe coding, and fine-tuning.

Core AI3 terms

Retrieval-Augmented Generation (RAG)

A technique that improves AI answers by retrieving relevant documents from an external knowledge base before generating a response.

Vector Database

A database optimized for storing and searching high-dimensional vector embeddings — the numeric representations that power semantic AI search.

Embeddings

Numerical vector representations of text (or images, audio) that capture semantic meaning — the foundation of AI search and memory.

Language Models2 terms

Context Window

The maximum amount of text an AI model can process in a single interaction — its working memory.

Large Language Model (LLM)

A deep learning model trained on vast text data that can understand and generate human language across a broad range of tasks.

AI Agents3 terms

AI Agent

An AI system that perceives its environment, makes decisions, and takes actions autonomously to achieve a goal.

Agentic AI

AI systems that can autonomously plan, act, and iterate toward goals over multiple steps without human intervention at each step.

Multi-Agent System

An AI architecture where multiple specialized AI agents collaborate, each handling a sub-task, to complete complex goals no single agent could handle alone.

Dev Tools3 terms

Model Context Protocol (MCP)

An open standard that lets AI models connect to external tools, data sources, and services through a unified interface.

Vibe Coding

A coding approach where you describe what you want in natural language and let an AI write the code, focusing on intent rather than syntax.

Function Calling (Tool Use)

The ability of an LLM to call external functions or APIs as part of generating a response, enabling it to interact with the real world.

MLOps2 terms

Fine-tuning

Further training a pre-trained AI model on a specific dataset to specialize it for a particular task or style.

MLOps

The practice of deploying, monitoring, and maintaining machine learning models in production — the DevOps of AI.

Prompting3 terms

Prompt Engineering

The practice of designing and refining inputs to AI models to get the most useful, accurate, and consistent outputs.

System Prompt

The hidden instructions given to an AI model at the start of a session that define its role, behavior, constraints, and persona.

Chain-of-Thought Prompting

A technique where you instruct an AI to reason through a problem step by step before giving its final answer, improving accuracy on complex tasks.

Safety & Alignment3 terms

AI Evals (Evaluations)

Systematic methods for measuring AI model and application quality — the testing framework for AI products.

AI Hallucination

When an AI model confidently generates false or fabricated information that is not supported by its training data or provided context.

AI Alignment

The challenge of ensuring AI systems pursue goals that are beneficial to humans, rather than goals that are misspecified, harmful, or contrary to human values.

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