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HomeGlossary
Glossary

AI terms, in plain English

No jargon for jargon's sake. Clear, honest definitions of the AI concepts that actually matter when you're building a real product.

Summarize with AI:ChatGPTClaudePerplexity

Agentic AI

Agentic AI is software that pursues a goal by reasoning, planning, and taking actions across tools and systems — not just answering a prompt.

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Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique where an AI model retrieves relevant information from your own data and uses it to ground its answer — so responses are accurate and current instead of guessed.

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Large Language Model (LLM)

A Large Language Model (LLM) is an AI model trained on vast amounts of text to understand and generate human language, powering tasks like writing, summarizing, extraction, and reasoning.

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Multi-Agent Orchestration

Multi-agent orchestration is coordinating several specialized AI agents — each handling part of a complex workflow — so they work together reliably toward one outcome.

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Intelligent Document Processing (IDP)

Intelligent Document Processing (IDP) is the use of AI to read, extract, verify, and route information from documents — like invoices, KYC forms, or bank statements — with little or no human effort.

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Computer Vision

Computer vision is AI that interprets images and video — detecting objects, defects, text, or patterns — turning camera feeds into structured, actionable data.

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MLOps

MLOps is the set of practices for deploying, monitoring, and maintaining machine-learning systems in production reliably — the discipline that keeps AI working after launch.

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Fine-Tuning

Fine-tuning is further training a base AI model on your own domain data so it performs better on your specific tasks, tone, and terminology.

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AI Pipeline (Data Pipeline)

An AI (or data) pipeline is the automated flow that collects, cleans, and feeds data into an AI model and delivers its output to where work happens — the plumbing that makes a model useful.

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Vector Database

A vector database stores data as numerical embeddings so an AI system can find information by meaning rather than exact keywords — the retrieval engine behind most RAG systems.

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Robotic Process Automation (RPA)

Robotic Process Automation (RPA) is software that mimics the clicks, keystrokes, and data entry a person does across applications — running repetitive, rule-based tasks automatically and without errors.

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AI Agent

An AI agent is a piece of software that can take actions to complete a task — looking up data, calling tools, and working through multiple steps — rather than only answering a question.

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Workflow Automation

Workflow automation is connecting the steps of a business process — approvals, handoffs, data entry, notifications — so they run automatically instead of relying on people to move each one forward by hand.

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Optical Character Recognition (OCR)

Optical Character Recognition (OCR) is technology that turns text in images and scans — a photographed invoice, a PDF, a form — into digital text a computer can read, search, and process.

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Demand Forecasting

Demand forecasting is using historical data and patterns to predict how much of a product or service customers will want in the future — so you can stock, staff, and plan for it instead of guessing.

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Predictive Maintenance

Predictive maintenance is using data from equipment to predict when a machine is likely to fail — so you fix it just before it breaks, instead of on a fixed schedule or after a costly breakdown.

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Machine Learning (ML)

Machine learning (ML) is software that learns patterns from examples rather than being explicitly programmed with rules — so it can make predictions or decisions on data it hasn't seen before.

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Deep Learning

Deep learning is a type of machine learning that uses layered neural networks to learn complex patterns directly from raw data — the technique behind image recognition, speech, and modern language models.

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Anomaly Detection

Anomaly detection is AI that learns what "normal" looks like in your data and automatically flags anything unusual — a fraudulent transaction, a failing machine, a security breach — the moment it happens.

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Human-in-the-Loop (HITL)

Human-in-the-loop is a design where AI handles the routine work automatically but hands off to a person for the cases that need judgment, approval, or oversight — combining speed with accountability.

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