AI Glossary
Plain-English definitions of AI and automation terms.
A
Agentic AI
Agentic AI plans, acts, and adapts toward goals on its own. But agentic is a per-decision calibration, not a product you buy. The 2026 definition operators use.
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.
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.
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.
AI Personalization
AI personalization uses machine learning to deliver individualized experiences at scale. Learn the three architectures, CPG examples, and real ROI data.
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
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.
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
E
F
G
H
K
L
M
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.
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
P
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.
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
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.
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.