Glossary

The AI tools glossary for businesses

Plain-English definitions of the AI tools and business terms that come up as you build.

AI agent

An AI system that can take actions on its own to reach a goal - not just answer a question, but read, decide, and do multiple steps.

Prompt

The instruction you give an AI tool. The clearer you describe the outcome you want, the better the result you get back.

Prompt engineering

The skill of writing instructions that get useful results from AI tools - mostly a matter of clarity about the outcome, not technical tricks.

Workflow automation

Connecting your apps so a repetitive, multi-step task runs itself - often with AI steps handling the parts that need a little judgement.

No-code

Building software without writing code, using visual tools. AI has pushed this further - you can now describe what you want in plain English.

LLM (large language model)

The kind of AI model behind most current tools - trained on huge amounts of text to understand and generate language, code, and structured answers.

Prototype

A quick, rough version of an idea built to test it. AI tools make prototypes cheap and fast, so you can check demand before you build for real.

Productized service

A repeatable deliverable sold at a fixed scope and price, instead of scoping every project from scratch - easier to price, deliver, and scale.

MCP (Model Context Protocol)

An open standard that lets AI tools connect to your other apps and data sources in a consistent way, so an agent can act across the tools you use.

API

A way for software to talk to other software. AI tools use APIs to connect to your apps; automations wire APIs together so work flows between them.