Working with an AI assistant on a design task is itself an interface problem. The prompt is the control surface: what you type determines what the system can do for you, and vague input produces generic, unusable output. In this topic we treat prompting as a skill with structure — giving the model role and context, stating the task and constraints, specifying the output format, and supplying examples when the format matters.
For interface work, prompts also need domain content. A prompt that asks for "a login screen" gets you a cliche; a prompt that names the target users, the platform, the design system tokens, the WCAG contrast and focus requirements, and the error states you must handle gets you something you can actually critique and refine. Refinement is the normal case, not a failure: you read the output, find where it drifted from your intent, and change one thing in the prompt to close that gap.
The three videos below move from practical technique to a research view of why prompting is harder than it looks. While watching, collect concrete patterns you can reuse — ways to supply context, to constrain output format, to ask the model to reason or to critique its own work — and note the failure modes: over-trusting a confident answer, changing many things at once between attempts, and assuming the model shares background knowledge you never stated.
GitHub's tutorial introduction to prompt engineering for developers, aimed at people who are getting vague or unusable answers from large language models. Based on its title and description, it covers the essentials of how LLMs interpret input and the components of a prompt that reliably improve results, such as supplying context, being specific about the task, and shaping the output.
It comes from GitHub, a producer of widely used AI developer tooling, and it maps directly onto the Principles of Effective Prompting subtopic with a developer-facing vocabulary you will reuse when prompting for code scaffolding.
A Visual Studio Code video presenting four prompting techniques that generalize across AI tools and tasks, not just coding. Expect concrete, reusable prompt patterns demonstrated on practical examples, with attention to how rephrasing or adding structure changes the quality of the response.
The focus on a small set of transferable techniques gives you a starting toolkit for iterative prompt refinement and for embedding design system or requirement details into prompts, and it comes from the team behind a mainstream AI-assisted editor.
A conference talk video from ACM SIGCHI presenting the research paper "Why Johnny Can't Prompt," a study of how people without AI expertise attempt to design prompts. It reports where non-experts go wrong — such as over-generalizing from a single success, treating the model like a person who shares their assumptions, and refining prompts opportunistically rather than systematically.
This is peer-reviewed HCI research presented by its authors, and it directly addresses Avoiding Common Prompting Pitfalls and Iterative Prompt Refinement by naming the specific habits that make prompting fail.
Together these videos build the prompting competence the module outcomes depend on. The GitHub and VS Code tutorials give you the structural patterns needed to apply AI tools to generate layouts, content drafts, or code scaffolding that actually reflect your project requirements, which supports MO2, while also clarifying what these assistants can and cannot do in a design workflow (MO1). The SIGCHI research talk supports MO3 by showing that prompt outputs and prompting strategies both need deliberate evaluation: recognizing common failure patterns is what lets you judge AI-generated design output against HCI principles and usability standards rather than accepting the first plausible result.
These videos were chosen from YouTube on October 3, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.