Glossary

An AI model is the learned core of any AI system—but it's not the whole thing. What a model is, the main types, and why the system around it matters more.

Generative AI creates new content rather than just analyzing it. What it is, how it differs from predictive AI, and where it actually pays off.

Training data is what an AI model learns from—and its quality caps the model's quality. What it is, why 'garbage in, garbage out' rules AI, and how to get it right.

Edge AI runs models on the device, not in the cloud. What it is, why it helps with latency and privacy, and what you trade for it.

AI alignment is about making AI do what we actually want—safely and as intended. What it means, why it matters, and the practical version in your product.

MLOps is what keeps machine learning working after launch. What it is, what it covers, and why models fail in production without it.

Data labeling is the unglamorous work that makes supervised AI possible. What it is, why label quality caps model quality, and how to do it right.

Model deployment is where most AI projects stall. What it means to deploy a model, why it's harder than modeling, and what reliable serving requires.

An AI pipeline turns one-off model work into a repeatable, reliable flow. What it is, what stages it includes, and why it's key to scaling AI.

Synthetic data is artificially generated data used to train AI. What it is, when it helps with scarcity or privacy, and where its limits lie.

A reranker is a quiet fix for bad RAG answers. What it does, why reordering retrieved results matters, and how it lifts accuracy.

AI benchmarks rank models on standard tests—useful but misleading. What they are, why they don't predict your results, and what to measure instead.

An AI copilot works alongside a person, suggesting and drafting while they stay in control. What it is, how it differs from an agent, and why it's a safe first step.

Real AI value rarely comes from one prompt—it comes from workflows. What an AI workflow is, how it chains steps, and why it's how AI gets work done.

Agent orchestration coordinates multiple AI agents and steps toward a goal. What it is, why it enables complex work, and why control matters most.