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Reference

AI Glossary

Plain-English definitions of AI and automation terms.

25 terms

A

Glossary7 min read

Agentic AI

Agentic AI plans, acts, and adapts toward goals. In operations, the real work is calibrating which decisions the agent owns, surfaces, or leaves human.

Glossary3 min read

AI Fine-Tuning

AI fine-tuning adapts a pre-trained model to your specific task using labeled examples. Learn types (LoRA, QLoRA, DPO), costs, and when to fine-tune vs RAG.

Glossary5 min read

AI Hallucination

AI hallucination is when a language model produces a confident, fluent, factually wrong answer. Why it happens, where it shows up in production, and how to engineer around it.

Glossary3 min read

AI Observability

AI observability is the practice of monitoring AI model behavior, performance, and data quality in production. Learn key components, metrics, tools, and real-world examples.

Glossary3 min read

AI Personalization

AI personalization uses machine learning to deliver individualized experiences at scale. Learn the three architectures, CPG examples, and real ROI data.

Glossary3 min read

AI Supply Chain Optimization

AI supply chain optimization uses machine learning to predict demand, automate replenishment, and reduce stockouts. Learn how it works with real metrics.

C

Glossary4 min read

Computer Vision AI

Computer vision AI lets machines interpret visual data and take business decisions — defect detection, safety monitoring, retail analytics. Definition, applications, real ROI.

Glossary5 min read

Conversational AI

Conversational AI enables chat and voice workflows in enterprise systems. Learn how it differs from chatbots and when teams should automate or escalate.

D

Glossary3 min read

Document AI

Document AI uses machine learning to extract, classify, and understand data from documents. Learn how it differs from OCR and when to use it.

E

Glossary3 min read

Edge AI

Edge AI runs machine learning models directly on local devices instead of the cloud. Learn how it works, hardware options, enterprise use cases, and when to deploy.

F

Glossary4 min read

Federated Learning

Federated learning trains machine learning models across distributed data without centralizing it. Definition, architecture, enterprise use cases, and trade-offs.

G

Glossary

Generative AI

Generative AI is AI that creates new content — text, images, code, audio — from learned patterns. Learn how it works, enterprise use cases, and where it fits in your AI strategy.

H

Glossary3 min read

Human-in-the-Loop AI

Human-in-the-loop AI puts a person at defined checkpoints in an agent's workflow. Learn the four oversight modes and how operators decide which decision goes where.

K

Glossary4 min read

Knowledge Graph

A knowledge graph connects entities and relationships into a queryable structure that powers RAG, AI agents, and enterprise reasoning. Definitions, architecture, use cases.

L

Glossary4 min read

Large Language Models

LLMs are AI models that read and generate text at human level. Learn what they actually do, enterprise use cases with ROI data, hard limitations, and deployment considerations for enterprise buyers.

M

Glossary3 min read

MLOps

MLOps combines ML engineering, DevOps, and data engineering to deploy and maintain models in production. Learn the key components, tools, and why 87% of ML projects fail without it.

Glossary4 min read

Multimodal AI

Multimodal AI processes text, images, audio, and video in a single model. How it differs from text-only LLMs, enterprise use cases, and where it pays off.

N

Glossary3 min read

Natural Language Processing

NLP is the AI discipline that makes text machine-readable. Learn how it powers invoice automation, contract review, and support AI — with enterprise examples.

P

Glossary3 min read

Predictive Maintenance AI

Predictive maintenance AI uses sensor data and machine learning to predict equipment failures before they happen. Learn how it works, real examples, and when to implement.

Glossary3 min read

Prompt Engineering

Prompt engineering is the practice of designing inputs to LLMs that reliably produce accurate, useful outputs. Learn key techniques, enterprise use cases, and best practices.

R

Glossary3 min read

RAG (Retrieval-Augmented Generation)

RAG connects LLMs to external knowledge bases for accurate, grounded answers. Learn the architecture, chunking strategies, and when to use RAG vs fine-tuning.

Glossary3 min read

RLHF

RLHF is the training method that aligns LLMs with human preferences using a reward model. Learn the 3 stages, costs, and why DPO is replacing it in 2026.

S

Glossary3 min read

Synthetic Data

Synthetic data is artificially generated data that mimics real-world datasets. Learn generation methods, use cases for AI training, and key limitations.

T

Glossary3 min read

Transfer Learning

Transfer learning adapts pre-trained AI models to new tasks with less data and compute. Learn types, enterprise use cases, cost savings vs training from scratch.

V

Glossary4 min read

Vector Database

Vector databases store embeddings — numerical representations of text, images, and audio — and find similar items in milliseconds. The infrastructure layer underneath RAG, recommendation, and semantic search systems. Learn what they are, when you need one, and the production tradeoffs.