The input you give an AI — your question, instruction, or request. The quality of a prompt directly determines the quality of the output. Prompting is a learnable professional skill, not a technical one.

The practice of deliberately structuring your inputs to an AI to get better, more useful outputs. Prompt engineering is not about writing code — it's about communicating with precision. Every element of the seven-part framework is a form of prompt engineering.

The process of refining AI output through multiple follow-up prompts rather than accepting the first response. Professional AI workflows almost always involve iteration — treating the first output as a rough draft, then prompting further to improve, correct, or redirect it.

A technique where you provide one or more examples of what you want, and the AI uses them as a pattern to follow. Instead of describing a style in words, you show it. "Match the tone of this paragraph" is a form of few-shot prompting — and it is often more effective than any description you could write.

Asking the AI to perform a task with no examples — relying entirely on your written instructions. Most everyday prompting is zero-shot. It works well for clear, well-defined tasks. It struggles when the style, format, or nuance is hard to describe but easy to show.

A set of instructions given to an AI before the conversation begins, establishing its role, behavior, and constraints for the entire session. When a company deploys an AI assistant with a specific personality or set of limits, those are defined in a system prompt. As a user, you're working within whatever system prompt the platform has set — though you can usually override much of it with a strong enough role definition in your own prompt.

A workflow design principle where a human reviews, verifies, and makes final decisions on all AI-generated content before it is published or submitted. The AI drafts; the human decides. This is the professional standard — not optional. No framework, no matter how well-constructed, replaces editorial judgment.

LLMs learn from whatever text they were trained on. That text reflects the demographics, assumptions, and blind spots of its authors — which skews heavily toward English-language, Western, educated, and affluent sources. An AI asked to write for a specific community it has little training data on will produce output that feels generic, flattened, or subtly off. Recognizing this isn't a reason to avoid AI — it's a reason to edit critically and know your audience better than the model does.

The style and perspective an AI falls back on when no specific audience, role, or tone is specified. The default voice tends to be generic, formal, American-English, and corporate-adjacent. It is the voice of AI slop. Strong prompting — especially audience definition and constraints — is what moves the output away from the default and toward something that actually serves your readers.

A statement attached to any work product created with AI assistance, describing which tools were used and for what purpose. In this course, disclosure is required on all graded work. In professional practice, disclosure standards vary by outlet — but the direction of the industry is toward more transparency, not less. Getting comfortable with disclosure now puts you ahead of colleagues who are still hiding their AI use.