Prompt Engineering That Actually Works

Most PMs use AI like a search engine. This module rewires that. You'll learn systematic prompting techniques — chain-of-thought, few-shot, role framing, output structuring — with examples grounded in growth work at Mentimeter.

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The 5 layers of a great prompt

Role — who the model should be (“You are a senior growth PM…”)
Context — the situation, constraints, audience
Task — the single, specific outcome you want
Constraints — format, length, tone, things to avoid
Examples — 1–3 worked examples (few-shot)

Chain-of-thought

Adding think step by step forces the model to surface its reasoning before concluding. This catches logical errors and dramatically improves analytical tasks.

Output formatting

Ask explicitly for JSON or a markdown table when the output will be parsed or pasted into Notion. Structure is leverage — it makes downstream automation trivial.

Few-shot prompting

One great example beats a paragraph of instructions. For recurring PM tasks, keep a small library of input → output pairs you can paste into the prompt.

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Prompts — Copy the prompts