Simplify text to a target reading level
Sentence structure and vocabulary come down to the level you name; every number, date and named entity stays exactly as written.
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Your prompt
Model-agnostic — works in Claude, ChatGPT, Gemini or any AI chat.
Why this prompt works
- →"Simplify structure, not substance" is the exact instruction that separates real simplification from dumbing-down — without it, models cut the numbers along with the jargon.
- →The keep-the-term-plus-gloss rule handles words with no simpler synonym, instead of forcing a wrong or misleading substitute for them.
- →The can't-simplify-without-losing-a-fact clause catches the failure mode where precision quietly gets rounded away in the name of readability.
Part by part
- Simplify sentence structure and vocabulary, not the substance
- The distinction the whole prompt rests on. Simplification usually reaches for removing detail, which is a content change wearing an editorial label.
- keep every fact, number, date and named entity exactly as given
- Enumerates what must survive. Numbers are the first casualty of readability edits, because rounding genuinely does read more smoothly.
- do not round, drop or generalize them
- Names the three specific ways facts disappear, which is more enforceable than asking for accuracy.
- add a short plain-language gloss in parentheses
- The alternative to deleting a technical term. Removing the term makes the text simpler and leaves the reader unable to search for the concept.
- say so instead of quietly cutting the fact
- Forces the conflict to surface. Some sentences genuinely cannot be simplified without loss, and that is a decision for the author.
Common mistakes
Simplify this text.Simplification without a fact constraint removes detail, because detail is what makes text hard. Numbers get rounded, qualifiers vanish, and the result reads easily and says less.
Rewrite this for a general audience.General audience is interpreted as shorter, and shortening is achieved by cutting. Naming a reading level gives a target that is about sentence structure rather than length.
Make this easier to understand and remove the jargon.Removing a technical term entirely leaves the reader unable to look the concept up. Keeping the term with a gloss teaches the vocabulary rather than routing around it.
Questions
Why do numbers get changed so often?
Because rounding genuinely improves readability — "about 40%" is easier than "38.7%". That makes it a natural edit, and it is a factual change that no reader can detect without the original.
How do I specify a reading level usefully?
Name a grade or an audience with a readability implication, such as a general newspaper reader. Asking for "simple" gives no target, and the output drifts toward whatever the model considers simple.
Should the readability score be checked afterwards?
If the level matters formally, yes — models estimate reading level poorly. A Flesch-Kincaid check takes seconds and often shows the result is a grade or two off the request.
Is this appropriate for legal or medical text?
For a plain-language summary alongside the original, yes. As a replacement, no — those documents mean what they say precisely, and a simplified version that changes an obligation is a liability rather than a service.