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Glossary

AI terms, explained clearly

Concise, practical definitions of the terms you need before adopting AI.

Agentic AI

Agentic AI refers to AI systems that can plan and take multi-step actions toward a goal — using tools, calling APIs and making decisions — rather than just answering a single prompt. Agents perceive, reason, act and adapt with minimal human intervention.

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Retrieval-Augmented Generation (RAG)

RAG is a technique that grounds an AI model's answers in your own data. Before responding, the system retrieves relevant documents from a knowledge base and feeds them to the model — producing accurate, source-cited answers instead of generic or hallucinated ones.

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Large Language Model (LLM)

A Large Language Model is an AI trained on vast amounts of text to understand and generate human language. LLMs power chatbots, agents and copilots — they can summarize, translate, write, classify and reason over natural language.

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AI Agent

An AI agent is a software system powered by an LLM that can autonomously complete tasks — understanding a request, using tools and data, and taking actions across your systems. Examples include voice receptionists, support copilots and lead-qualification agents.

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AI Workflow Automation

AI workflow automation uses AI to run multi-step business processes end to end — combining tools like n8n or Make with LLMs to handle tasks that need understanding, such as reading documents, drafting replies and updating systems automatically.

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Fine-tuning

Fine-tuning is the process of further training a pre-trained AI model on your specific data so it performs better on your tasks, tone and domain. It's one option for customization — often RAG is faster and cheaper for knowledge-grounding.

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Vector Database

A vector database stores text and other data as numerical embeddings so an AI can find the most semantically relevant information quickly. It's the retrieval engine behind RAG and knowledge assistants.

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AI Governance

AI governance is the framework of policies, controls and oversight that keeps AI systems safe, compliant and accountable. It covers data privacy, security, bias, auditability and alignment with regulations like GDPR and Gulf PDPL.

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Prompt Engineering

Prompt engineering is the practice of designing the instructions, context and examples you give an AI model to get reliable, accurate results. It covers wording, structure, role-setting and few-shot examples — turning a general model into a dependable tool for a specific task.

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AI Orchestration

AI orchestration is the coordination of multiple AI models, tools and steps into a single reliable workflow. Unlike RPA, which follows fixed rules, orchestration routes tasks intelligently — deciding which model or tool to use, in what order, and how to handle the results.

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AI Copilot

An AI copilot is an assistant embedded in your tools and workflows that helps a person work faster — drafting, summarizing, answering questions and suggesting actions while the human stays in control. Unlike a fully autonomous agent, a copilot keeps a person in the loop.

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AI Hallucination

An AI hallucination is when a model produces confident but false or fabricated information — inventing facts, sources or details that aren't real. It happens because LLMs predict plausible text rather than retrieve verified truth, and it's the main risk to manage in production AI.

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Embeddings

Embeddings are numerical representations of text, images or other data that capture meaning, so an AI can measure how similar two things are. Words and documents with related meanings sit close together in this vector space — making embeddings the foundation of semantic search and RAG.

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Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard that lets AI models connect to external tools, data and systems through a common interface. Instead of custom integrations for each app, MCP gives agents a consistent way to read data and take actions across your stack.

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Conversational AI

Conversational AI is technology that lets people interact with software through natural language — by chat or voice. Powered by LLMs, it understands intent, holds context across a dialogue and responds helpfully, enabling chatbots, voice assistants and automated support that feel human.

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AI Automation

AI automation is the use of artificial intelligence to perform tasks that previously required human judgment — like reading documents, classifying requests, drafting responses and making decisions. Unlike rule-based automation, it handles unstructured information and adapts to variation without explicit programming for every case.

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