Glossary

Enterprise AI Glossary

Every AI term your team needs to know, explained in plain English. No jargon walls, no filler.

Agentic AI

Agentic AI describes AI systems that can independently plan, decide, and take actions to achieve a goal without step-by-step human instruction. These systems observe their environment, reason about what to do next, and execute multi-step workflows on their own.

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AI Agent

An AI agent is a software program that perceives its environment, makes decisions, and takes actions to accomplish specific goals. Unlike simple chatbots, AI agents can use tools, access external systems, and operate across multi-step workflows.

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AI Governance

AI governance is the set of policies, processes, and organizational structures that define how AI systems are developed, deployed, monitored, and retired within an enterprise. It covers accountability, risk management, compliance, ethics, and operational standards.

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AI Guardrails

AI guardrails are the rules, constraints, and safety mechanisms that keep an AI system operating within defined boundaries. They prevent the model from generating harmful content, taking unauthorized actions, or producing outputs that violate business rules.

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AI Hallucination

AI hallucination occurs when a language model generates information that sounds plausible and confident but is factually incorrect, fabricated, or not grounded in the provided context. It is one of the biggest reliability challenges in deploying AI for enterprise use.

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AI Observability

AI observability is the practice of monitoring, logging, and analyzing the behavior of AI systems in production. It gives teams visibility into model performance, latency, cost, error rates, and output quality so they can detect and fix problems quickly.

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Chunking (RAG)

Chunking is the process of splitting documents into smaller, meaningful segments before storing them in a vector database. The size and method of chunking directly affect the quality of retrieval in a RAG system, since the AI model can only work with the chunks it receives.

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Context Window

The context window is the maximum number of tokens a language model can process in a single request, including both the input prompt and the generated output. It defines how much information the model can consider at once when generating a response.

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Embedding (AI)

An embedding is a numerical representation of data (text, images, audio) as a vector of numbers. Embeddings capture the semantic meaning of content so that similar items have similar vectors, enabling AI systems to search, compare, and cluster information by meaning.

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Fine-Tuning

Fine-tuning is the process of further training a pre-trained AI model on your own dataset to adapt its behavior, knowledge, or style for a specific domain or task. It changes the model's internal weights, making it permanently better at your particular use case.

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Grounding (AI)

Grounding is the practice of connecting an AI model's outputs to verified, factual source data rather than letting it rely solely on its training knowledge. It ensures that generated responses are based on real documents, databases, or other authoritative sources.

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LLM Orchestration

LLM orchestration is the process of coordinating how a large language model interacts with tools, data sources, memory, and other models to complete a task. It defines the sequence of calls, handles errors, and manages the flow of information between components.

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Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard created by Anthropic that defines how AI models connect to external tools, data sources, and services. It provides a universal interface so that any tool can work with any model, similar to how USB standardized device connections.

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Multi-Agent Systems

A multi-agent system is an architecture where multiple AI agents, each with a specific role or skill, collaborate to accomplish a larger task. Agents can delegate work to each other, share context, and coordinate their actions.

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Prompt Engineering

Prompt engineering is the practice of designing and refining the instructions you give to an AI model to get more accurate, consistent, and useful outputs. It includes techniques like providing examples, setting roles, specifying formats, and breaking complex tasks into steps.

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Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a technique where an AI model first retrieves relevant documents from a knowledge base, then uses those documents as context to generate an accurate response. It reduces hallucination by grounding answers in real data.

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Semantic Search

Semantic search finds information based on the meaning of a query rather than exact keyword matches. It uses AI embeddings to understand intent and context, returning results that are conceptually relevant even when they use different words.

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Token (LLM)

A token is the basic unit of text that a large language model processes. It can be a word, part of a word, or a punctuation mark. Language models read, process, and generate text as sequences of tokens, and API pricing is based on the number of tokens used.

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Tool Use (Function Calling)

Tool use (also called function calling) is the ability of an AI model to invoke external functions, APIs, or services as part of generating a response. Instead of only producing text, the model can decide to call a tool, receive the result, and use it to complete the task.

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Vector Database

A vector database stores data as high-dimensional numerical vectors (embeddings) and enables fast similarity search across them. It is the core infrastructure behind RAG systems, semantic search, and recommendation engines in AI applications.

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