Reference

AI Glossary

Plain-English definitions of AI and automation terms.

15 terms

A

Agentic AI

Agentic AI is autonomous AI that plans, executes, and adapts multi-step workflows without human intervention. Learn the architecture, real examples, and how it differs from chatbots.

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.

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

Glossary3 min read

Computer Vision AI

Computer vision AI enables machines to interpret visual data and take action. Learn how it works, top business use cases, and real ROI numbers.

Glossary3 min read

Conversational AI

Conversational AI enables natural language interactions between humans and machines. Learn how it works, the architecture behind it, and real business ROI.

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.

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.

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.

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.