Learn AI in plain English.

Software that does tasks that normally take human judgment, such as reading, writing, recognizing speech or spotting patterns.

Software that learns patterns from examples instead of following hand-written rules. Predictive AI is machine learning.

AI that creates new text, images, audio or video, rather than only classifying or predicting.

The engine behind chat assistants: a model trained on vast amounts of text to predict and produce language.

A model that works with more than one kind of input or output, such as text, images and audio together.

The instructions or question you give an AI. Clearer prompts with examples produce better results.

The unit AI models read and bill by, roughly three-quarters of a word. Pricing is usually per million tokens.

How much text a model can consider at once, including your documents and the conversation so far.

When an AI states something false with confidence. The main reason to check its work.

Making an AI answer from specific, approved sources (your documents) instead of from memory, and cite them.

Further training a model on your own examples to change its style or skills. Rarely needed by small businesses.

AI that takes actions across several steps and tools to finish a job, within rules you set.

Rules and limits that keep an AI inside safe boundaries: what it may say, do or access.

A design where a person reviews or approves the AI's work before it takes effect.

A doorway an app provides so other software can read and write its data.

A message an app sends the instant something happens, so other systems can react.

Model Context Protocol: an open standard for connecting AI assistants to business apps.

The official sign-in screen that grants an app limited access to your account without sharing your password.

The company that makes the underlying AI model, such as OpenAI, Anthropic or Google. Many tools are built on their models.