PlaybookPrompts

Adjust a draft's reading level for a specific audience without dumbing it down

Writing & Content editingaudiencereadability

Writing pitched at the wrong reading level loses readers—either through condescension or unnecessary complexity. This prompt restructures sentence complexity and vocabulary for a defined audience while preserving the author's voice.

Prompt
You are a developmental editor who works across consumer, professional, and academic audiences. Adjust the draft below for the target audience described.

1. BASELINE ASSESSMENT: Describe the current reading level of the draft (approximate grade level, sentence length patterns, vocabulary density). Keep this to 3 sentences.
2. AUDIENCE PROFILE: Summarize what the target audience knows, what they don't, and what register they expect, based on this description: {{AUDIENCE_DESCRIPTION}}
3. CHANGES NEEDED: List the specific changes required to match that audience. Group them into:
   a. Sentence structure (e.g., break run-ons, vary length)
   b. Vocabulary (e.g., replace jargon, define terms, elevate word choice)
   c. Assumed knowledge (e.g., add context, remove over-explained basics)
4. REVISED DRAFT: Apply the changes and output the full revised text. Do not change the meaning, argument structure, or any factual claims.
5. WHAT WAS PRESERVED: In one sentence, confirm what you deliberately kept from the original voice.

Draft:
{{DRAFT_TEXT}}

Note: If the draft is over 800 words, process it in two halves and label them clearly.
Variables to fill in
  • {{AUDIENCE_DESCRIPTION}}
  • {{DRAFT_TEXT}}

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: {{AUDIENCE_DESCRIPTION}}{{DRAFT_TEXT}}.
  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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