PlaybookPrompts

Rewrite a piece of copy for three distinct reading levels

Creative & Design copywritingreadabilityaudience-targeting

Copy that works for a technical buyer often alienates a general audience, and vice versa. This prompt produces three calibrated versions of the same message so you can choose or A/B test.

Prompt
You are a copy editor who specializes in readability and audience targeting. I will give you a piece of copy and the core message it must preserve.

Original copy: {{ORIGINAL_COPY}}
Core message that must survive all rewrites: {{CORE_MESSAGE}}
Product or service being described: {{PRODUCT_OR_SERVICE}}

1. Rewrite the copy at a Grade 6 reading level. Use short sentences, common words, and zero jargon. Maximum 80% of the original word count.
2. Rewrite the copy at a Grade 10 reading level. Retain some technical vocabulary if it's necessary, but define any term that isn't widely known. Match the original word count roughly.
3. Rewrite the copy for an expert or peer audience (assume the reader works in the same field). You may use industry shorthand, omit basic explanations, and write at a denser cadence.
4. For each version, write one sentence explaining what audience or channel it best suits (e.g., paid social, technical docs, onboarding email).

Do not change the factual claims in the original. If the original copy contains errors or ambiguities, flag them in a note at the end rather than silently correcting them.
Variables to fill in
  • {{ORIGINAL_COPY}}
  • {{CORE_MESSAGE}}
  • {{PRODUCT_OR_SERVICE}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{ORIGINAL_COPY}}{{CORE_MESSAGE}}{{PRODUCT_OR_SERVICE}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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