AI Glossary for SMBs

Computer systems that perform tasks normally requiring human intelligence — understanding language, recognizing patterns, making decisions, and generating content.

A branch of AI where software learns patterns from data and improves with experience, instead of following hand-written rules.

A type of machine learning that uses many-layered neural networks to learn complex patterns from large amounts of data.

A model loosely inspired by the brain, made of interconnected “nodes” that pass signals to recognize patterns.

A set of step-by-step instructions a computer follows to solve a problem or complete a task.

The trained product of a machine-learning process — a file that takes an input and produces an output (text, a prediction, an image).

The examples used to teach a model. Quality and relevance of this data largely determine how well the model performs.

The act of running a trained model to get an answer — i.e., actually using the AI, as opposed to training it.

A hypothetical future AI that matches or exceeds human ability across virtually any task. It does not exist yet.

AI that creates new content — text, images, audio, video, or code — rather than just classifying or predicting.

A model trained on vast amounts of text that can understand and generate human-like language.

A large, general-purpose model trained on broad data that can be adapted to many tasks.

AI that works across more than one type of input or output — text, images, audio, and video together.

The small chunks of text (roughly ¾ of a word) that language models read and write. Usage and pricing are measured in tokens.

The maximum amount of text (in tokens) a model can consider at once — its short-term memory for a single conversation or document.

A numerical representation of text (or images) that captures meaning, so a computer can measure how similar two things are.

The field focused on helping computers understand and work with human language.

Proprietary models are owned and hosted by a vendor (you pay per use); open-source models are freely available to run or modify yourself.

The largest, most capable models at the cutting edge of what AI can do, released by a few well-funded labs.

A compact, efficient model that runs faster and cheaper than a frontier model, sometimes even on a phone.

The instruction or question you give an AI to get a response.

The practice of crafting and refining prompts to reliably get high-quality, useful outputs.

Behind-the-scenes instructions that set an AI's role, tone, and rules for an entire conversation.

A setting that controls how creative vs. predictable a model's output is. Low = focused and consistent; high = varied and creative.

Prompting a model to reason step by step before giving its final answer.

A newer class of model that spends extra time “thinking” through a problem before answering, trading speed for accuracy.

Giving a model a handful of examples inside the prompt so it follows the pattern you want — no retraining required.

Asking AI to do a task with no examples — just a plain instruction — and trusting it to figure it out.

Structured tests that measure how well a model or AI feature performs on the tasks you actually care about.

An AI system that can take actions to accomplish a goal — browsing the web, running code, calling other software, and completing multi-step tasks.

AI systems that work autonomously toward goals, making decisions and chaining steps together rather than waiting for each instruction.

An AI's ability to use external tools — search, a calculator, your CRM, a calendar — by calling them as functions.

An open standard that lets AI assistants securely connect to your apps and data sources through a common “plug.”

Coordinating multiple AI steps, models, or agents so they work together in a reliable sequence.

A setup where AI does the work but pauses for a person to review or approve before key actions happen.

An AI that works alongside a person inside their existing tools, suggesting and helping rather than acting fully on its own.

Using software (increasingly AI-driven) to run repetitive, multi-step business processes with little manual effort.

Several specialized AI agents working together, each handling part of a job and passing results to the next.

AI that operates a real computer or web browser — clicking, typing, and navigating apps like a person would.

A technique that lets an AI look up relevant information from your own documents before answering, instead of relying only on its training.

Further training an existing model on your specific examples so it specializes in your tasks, tone, or domain.

A database that stores embeddings so AI can find information by meaning, not just exact keywords.

The curated collection of documents and facts an AI draws on to answer questions about your business.

Tying an AI's answers to verified, real sources so responses are accurate and traceable.

A standard way for software systems to talk to each other and exchange data or services.

A program that converses with users in natural language, today usually powered by an LLM.

Structured data is neatly organized (spreadsheets, databases); unstructured data is free-form (emails, PDFs, images, calls).

Artificially generated data that mimics real data, used to train or test AI without exposing sensitive records.

AI that analyzes historical data to forecast future outcomes.

Using AI to detect the emotional tone behind text — positive, negative, or neutral.

AI that interprets images and video — recognizing objects, reading text, or detecting defects.

When an AI states false information confidently as if it were fact.

Systematic unfairness in AI outputs, usually inherited from skewed or unrepresentative training data.

Rules and filters that keep an AI's behavior within safe, approved boundaries.

The policies and oversight that ensure AI is used responsibly, legally, and in line with company values.

Protecting personal and confidential information, including how it's handled when sent to AI tools.

An attack where hidden malicious instructions trick an AI into ignoring its rules or leaking information.

Employees using unapproved AI tools at work without oversight.

AI-generated fake video, audio, or images that convincingly imitate a real person.

Emerging laws — led by the EU AI Act — that set rules for how AI can be built and used, by level of risk.

How clearly an AI system can show why it produced a particular answer or decision.