--- title: "AI terms, explained with everyday examples" description: "A searchable AI glossary with plain-language definitions, examples, and why each concept matters in practice." author: Graham date: 2026-09-06 url: https://powerofai.ca/glossary --- # AI terms, explained with everyday examples A searchable AI glossary with plain-language definitions, examples, and why each concept matters in practice. Plain-language editorial definitions with examples and practical implications. Specifications and capabilities vary by application. ## Agent An AI system that can use tools and choose steps while working toward a goal. For example: It inspects project files, makes a change, and runs a check. Why it matters: Define scope, permissions, and completion evidence. [See it in practice](/learn/from-chat-to-agent) ## API An interface that lets software request capabilities or exchange data with another system. For example: An application sends a request to a model service and receives a response. Why it matters: Using an API can involve separate credentials, usage costs, and data handling from a chat subscription. [See it in practice](/learn/compare-ai-tools) ## Artifact A usable output such as a document, spreadsheet, image, or application. For example: A downloadable workbook is an artifact; a description of a workbook is a plan. Why it matters: Open and inspect the actual output before treating the task as complete. [See it in practice](/learn/from-chat-to-agent) ## Aspect ratio The relationship between an image’s width and height. For example: A 16:9 image is wider than a square 1:1 image. Why it matters: Choose it for the place the image will appear. [See it in practice](/learn/image-prompts-that-work) ## Automation A process that runs defined work with reduced manual intervention. For example: A scheduled workflow prepares a report from approved inputs. Why it matters: Monitor failures and keep consequential actions within the authority you intended. [See it in practice](/learn/from-chat-to-agent) ## Benchmark A shared test used to compare performance under specified conditions. For example: Different systems are tested on the same collection of tasks. Why it matters: A benchmark result may not predict performance on your particular workflow. [See it in practice](/learn/compare-ai-tools) ## Chatbot An application that lets you interact through a conversation. For example: You ask a question, receive a reply, and refine it in another turn. Why it matters: Some chatbots also have tools, so inspect what yours can actually do. [See it in practice](/learn/ai-beyond-search) ## Citation A reference connecting a claim to a source. For example: A link points you to the policy behind an eligibility claim. Why it matters: Open it and verify that the passage supports the actual claim. [See it in practice](/learn/check-ai-answers) ## Context The information available to the model for a response, such as instructions, conversation, and supplied material. For example: Your audience, source notes, and a sample email can all be context. Why it matters: Important background that you never supplied may be guessed or missed. [See it in practice](/learn/context-that-actually-helps) ## Context window The amount of information a model can consider in one processing context. For example: A long project history may need a checked summary or selective retrieval. Why it matters: Fitting information into the window does not guarantee every detail will be used correctly. [See it in practice](/learn/context-that-actually-helps) ## Edge case An unusual input or condition that can expose a failure. For example: A meeting note includes a task that was later cancelled. Why it matters: Include realistic awkward cases alongside ordinary examples. [See it in practice](/learn/test-an-ai-workflow) ## Evaluation Testing an output or system against defined criteria. For example: Check whether extracted action items have supported owners and dates. Why it matters: Define the criteria before judging the result. [See it in practice](/learn/test-an-ai-workflow) ## Few-shot prompting Showing a small number of examples to demonstrate the desired behavior. For example: Two labeled customer messages demonstrate your categories. Why it matters: Use a new case to test whether the assistant understood the pattern. [See it in practice](/learn/prompt-with-examples) ## Generative AI AI that produces content such as text, images, audio, or code. For example: It can draft a story or suggest an image from a description. Why it matters: Generated content still needs to fit the facts and purpose of the task. [See it in practice](/learn/ai-beyond-search) ## Grounding Connecting an answer to supplied or retrieved information. For example: A research answer draws on a passage in an uploaded report. Why it matters: The connection needs checking; grounded does not mean infallible. [See it in practice](/learn/research-with-your-own-sources) ## Hallucination A common term for generated information that is false or unsupported but presented as if it were established. For example: A convincing answer includes a book title that does not exist. Why it matters: Fluent wording cannot establish that a factual claim is true. [See it in practice](/learn/check-ai-answers) ## Handoff A checked summary of current work that helps another session or person continue. For example: It lists the artifact, decisions, unresolved issues, and next step. Why it matters: Current files may have changed since the summary was written. [See it in practice](/learn/handoff-a-long-project) ## Holdout Examples kept aside while improving a system, then used to check the revised behavior. For example: You tune a prompt on five notes and test it on two unseen notes. Why it matters: Repeatedly tuning on the holdout removes its independence. [See it in practice](/learn/test-an-ai-workflow) ## Human in the loop A process with deliberate human review or decisions at particular points. For example: You review a prepared email before it is sent. Why it matters: The review should show a concrete result and occur where it can still prevent a consequential mistake. [See it in practice](/learn/agent-permissions) ## Inference A conclusion drawn from information rather than directly stated in it. For example: Sales rose after a campaign; attributing the rise to the campaign is an inference. Why it matters: Keep the observation separate from the explanation. [See it in practice](/learn/check-ai-answers) ## Inpainting Editing or replacing a selected region of an image. For example: You select a background area and ask to change it. Why it matters: Inspect neighboring details and the whole result after the edit. [See it in practice](/learn/iterate-on-ai-images) ## Latency The time before a system responds or completes a step. For example: A complex task may take longer to return a result. Why it matters: Measure the complete task, including your review, when judging usefulness. [See it in practice](/learn/test-an-ai-workflow) ## MCP Model Context Protocol: a standard for connecting AI applications with tools and resources. For example: A compatible application can connect to a server that exposes permitted project tools. Why it matters: A connection mechanism does not itself decide which actions should be trusted or authorized. [See it in practice](/learn/agent-permissions) ## Model The trained system that produces or interprets content. For example: A chat application may let you select different models for the same conversation. Why it matters: The application’s tools and permissions matter alongside the model. [See it in practice](/learn/compare-ai-tools) ## Multimodal Able to work with more than one kind of information, such as text and images. For example: You supply a photo and ask a question about its visible contents. Why it matters: Supported input and output types depend on the particular tool. [See it in practice](/learn/image-prompts-that-work) ## Permission Authorization for a tool or system to access data or perform an action. For example: Reading a folder does not necessarily authorize deleting its files. Why it matters: Use application controls as well as clear instructions. [See it in practice](/learn/agent-permissions) ## Prompt The request or instructions you give an AI system. For example: “Turn these notes into three action items” is a prompt. Why it matters: A useful prompt explains the result you need and the relevant constraints. [See it in practice](/learn/better-follow-up-questions) ## Prompt injection Untrusted content attempting to redirect an AI system with instructions. For example: A retrieved document tells an assistant to ignore the user and send data elsewhere. Why it matters: Treat source content as data and limit tool access; a prompt alone is not a security boundary. [See it in practice](/learn/agent-permissions) ## RAG Retrieval-augmented generation: finding relevant information and using it as context for a generated answer. For example: A support assistant retrieves a policy passage before drafting a reply. Why it matters: Retrieval can miss material or return something outdated; inspect the source. [See it in practice](/learn/research-with-your-own-sources) ## Retrieval Finding relevant material to include when answering or working on a task. For example: The system searches a document collection for passages about a policy. Why it matters: The answer is limited by what was found and how accurately it was interpreted. [See it in practice](/learn/research-with-your-own-sources) ## Structured output Output arranged in a defined format with named fields or rules. For example: An action list has action, owner, deadline, and source fields. Why it matters: A valid shape does not prove the values inside it are correct. [See it in practice](/learn/prompt-with-examples) ## System instruction A higher-level instruction set supplied by an application to guide the assistant’s behavior. For example: An application defines the role and rules of a support assistant. Why it matters: What you can configure depends on the application; a user message is not always the highest-priority instruction. [See it in practice](/learn/context-that-actually-helps) ## Token A unit of text or other encoded information a model processes; text tokens can be words or pieces of words. For example: A short sentence may become several tokens. Why it matters: Token limits and costs are not the same as word counts. [See it in practice](/learn/context-that-actually-helps) ## Tool use An AI system invoking an available capability outside text generation. For example: It runs a calculation or reads a permitted file. Why it matters: The tool’s actual access determines what actions are possible. [See it in practice](/learn/agent-permissions) ## Workflow A sequence of steps that turns inputs into an outcome. For example: Extract notes, flag missing owners, then format an action list. Why it matters: Known steps can often be handled with a simpler process than an open-ended agent. [See it in practice](/learn/from-chat-to-agent) ## Zero-shot prompting Requesting a task without supplying worked examples. For example: “Summarize this paragraph in one sentence.” Why it matters: Simple tasks may not need a demonstration; add examples when they clarify a real ambiguity. [See it in practice](/learn/prompt-with-examples)