Write a myth-busting article that corrects common misconceptions
A credible, engaging article that names popular myths about a topic and replaces each with what is actually true.
The prompt
Write a myth-busting article about my topic.
Topic: [TOPIC — e.g. "credit scores", "composting", "remote work productivity"]
Audience: [WHO and what they already believe]
My expertise or angle: [why readers should trust me]
Myths I've heard: [LIST, or "suggest the most common ones"]
Sources I have: [paste studies, data, or official guidance, or "none yet"]
Length and format: [e.g. "1,200-word blog post", "LinkedIn carousel", "newsletter section"]
First, suggest 8-10 candidate misconceptions, and for each say why people believe it and how confident we can be that it is wrong. Then write the article using the 5-7 strongest ones.
For each myth:
- Myth: stated fairly, the way people actually say it.
- Why it sticks: the grain of truth or experience behind it.
- What's actually true: the correction in plain language, with a specific example.
- Evidence: a source from what I provided, or [ADD SOURCE] where one is needed.
- What to do instead: one practical takeaway.
Rules:
- Do not mock readers who held the myth; correct without condescension.
- Do not invent studies, statistics, or quotes.
- If the evidence is mixed, say "partly true" rather than forcing a verdict.
- For health, legal, or money topics, add a note to check with a qualified professional for personal situations.
Also give me 3 headline options and a short meta description.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Content & Social Media prompts
What to fill in
- WHO and what they already believe
- why readers should trust me
- LIST, or "suggest the most common ones"
- paste studies, data, or official guidance, or "none yet"
- ADD SOURCE
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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