Week 4 — Readings & Resources · Prompting II — Meta-Prompting & Structured Prompts

Course: Using Artificial Intelligence (AI 101) · Silver Oak University (fictional sample) · Prof. Quinn
Objective covered: Objective 2 — Use effective prompting techniques — including meta-prompting and structured-prompt components — to produce high-quality, well-verified AI results.

How to use this page

Everything here is a link to an external resource — open it in your browser. Nothing is downloaded or redistributed.

This week's load is deliberately focused: 2 readings + 1 video, grouped by the two big skills. Do one per group and you're ready for the quiz; do all of them and you'll be very comfortable. Total time: roughly 30–40 minutes.

Order that matches the lecture: ① what prompt engineering actually is → ② the structured components → ③ the tools you'll build in.

A habit to reinforce now: notice how each of these readings is itself structured. What is the author's goal? Who is their audience? What constraints did they impose on length and tone? Reading for structure is part of learning to write structured prompts.

① What Prompt Engineering Actually Is

Maps to Lecture Segment 2 (meta-prompting) and the week's framing. Understanding what makes a prompt work — and that it's a learnable discipline, not magic — is the prerequisite for the nine-component framework.

Reading — "Prompt engineering" (Wikipedia)
🔗 https://en.wikipedia.org/wiki/Prompt_engineering
Why it's assigned: a reliable, always-available overview of what prompt engineering means — the idea that the way you phrase a request has systematic, predictable effects on output. Skim the introduction and the "Techniques" section; the rest is reference material you can return to. (Skim, ~10 min.)

Reading — "Introduction to prompt design" (Google AI for Developers)
🔗 https://ai.google.dev/gemini-api/docs/prompting-intro
Why it's assigned: Google's own plain-language intro to how prompts work and why structure matters — components, role, context, examples, and evaluation are all visible here. Written for a wide audience; no coding required to follow it. (~10 min.)

② The Structured-Prompt Components in Practice

Maps to Lecture Segment 3 and the table (Context · Role · Goal · Audience · Constraints · Voice/Format · Data/Logic · Examples · Evaluation). Seeing the components in the context of a real model's guidance shows students they're naming ideas that the AI's own makers teach.

Reading — "Prompt design strategies" (Google AI for Developers)
🔗 https://ai.google.dev/gemini-api/docs/prompting-strategies
Why it's assigned: goes into the components in practice — giving examples, setting output format, constraints, and evaluation. Complements the lecture's nine-component table. (~12 min.)

③ The Tools You Build In (the approved assistants)

You'll use one of these for the tutorial, discussion, assignment, and Studio this week. These are the official product pages.

• ChatGPT (OpenAI) 🔗 https://chatgpt.com
• Claude (Anthropic) 🔗 https://claude.com
• Gemini (Google) 🔗 https://gemini.google.com
• Copilot (Microsoft) 🔗 https://copilot.microsoft.com

(Free tiers are sufficient for every Week 4 activity.)

Pick-one quick path (≈ 12 min total)

In a hurry? Do exactly these two and you'll be ready for the quiz:
1. Skim "Prompt engineering" (Wikipedia) — just the introduction and Techniques section (group ①).
2. Read "Prompt design strategies" (Google AI for Developers) (group ②).

Heads-up (links rot): these point to outside sites that may move or update. If a link fails, search for the title directly on the source site. Nothing here is downloaded or redistributed — all resources stay as links to their original sources.