Clear answers to the AI terms people mix up.

Artificial intelligence (AI) is the broad field of making computer systems perform tasks associated with human intelligence, such as recognizing language, making predictions, or planning. Machine learning (ML) is one way to build AI: it learns patterns from examples rather than relying only on hand-written rules. ML is part of AI, not a synonym for all AI.

A rule-based system follows instructions written by people: “if an invoice is over this amount, send it for approval.” A machine-learning system learns a pattern from data: it might estimate which invoices are unusual from prior examples. Rules are often better when the policy is explicit and non-negotiable; ML helps when the pattern is too complex to write as rules.

Generative AI creates new content: text, images, audio, code, summaries, or drafts. A chatbot is a common interface for generative AI, but the model can also sit inside a workflow that drafts a service reply or turns a meeting into a structured report.

A chat answers a prompt. A copilot helps a person produce or assess work while that person owns the decision. A workflow follows a designed sequence of steps and rules. An agent can select among permitted tools and steps in pursuit of a goal. The important business choice is the level of autonomy, not the product label.

No. A chat or copilot is often better for work that needs judgment, explanation, or careful approval. Agents add value when a task involves multiple steps, changing information, and permitted actions across systems. They also create more responsibility for permissions, monitoring, and recovery when something fails.

A model is the statistical engine that turns an input into an output. A language model can write, summarize, classify, or reason over text. Other models can forecast demand, recognize defects in images, or transcribe speech. A useful business application combines a model with context, rules, tools, people, and controls.

Training is how a model learns general patterns before you use it. Prompting is the instruction you give it for one task. Context is the evidence and state provided with that instruction: a policy, customer history, permissions, current records, or previous steps. In business, improving context is often more valuable than rewriting a prompt.

Retrieval-augmented generation (RAG) finds relevant, authorized documents or records before asking a model to answer. It helps a system ground its response in current internal knowledge and show the user the sources. It does not guarantee correctness: retrieval can still miss, select stale evidence, or expose information to the wrong person if the system is poorly designed.

An embedding is a numeric representation of the meaning of text, images, or other data. Vector search uses those representations to find items that are similar in meaning, even if they use different words. It is commonly used inside RAG, alongside filters for permissions, dates, document type, and business rules.

A hallucination is an output that sounds plausible but is unsupported or wrong. It is not a rare edge case to ignore; it is a failure mode to design for. For important work, require evidence, let the system say “I do not know,” use validation rules, and give people a clear path to correct or escalate.

Human-in-the-loop means a person reviews, approves, corrects, or overrides an AI output at a defined point in the process. It is most useful when the person has the right evidence, knows what they are accountable for, and can actually change the outcome. A vague final check is not meaningful oversight.

No. A bigger model may be more capable on a hard task, but it can cost more, respond more slowly, or still fail without good data and context. Compare models on realistic cases using quality, latency, cost per successful task, source grounding, and the amount of human review needed.

Guardrails are the boundaries that keep an AI system within its intended use. They include permissions, data filters, structured outputs, policy checks, approval gates, rate limits, tool restrictions, logging, and rollback procedures. Good guardrails make the safe path the easiest path for the system to follow.

Choose one bounded, high-volume workflow with a clear owner and a measurable baseline. Begin with retrieval, drafting, or recommendations; keep consequential actions behind approval; test representative cases; and use the results to decide whether to redesign, scale, or stop. The How-to page and AI Implementation Canvas ↗ provide a practical starting structure.