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Tighten Wordy Prose Without Losing Meaning

Trims wordy text for concision while preserving meaning, voice, and a target word-count reduction.

Optimized for
ChatGPTClaude
§ When to use this

Wordy prose is the most common reason good ideas read as boring or amateurish. Readers feel the drag even when they can't name it: hedges like "it could be argued that," doubled phrases like "each and every," and throat-clearing intros that take three sentences to reach the point. This prompt puts an editor's scalpel in your hands without asking you to memorize a rulebook. It targets a specific reduction, so the model commits to real cuts instead of nibbling, and it forces a before/after word count so you can verify the work rather than trust it. The change log matters as much as the rewrite: by seeing which patterns got cut and why, you start internalizing them and catching your own padding next time. Use it on anything that feels bloated, from a homepage paragraph to a wandering email. Keep your [DOMAIN] term accurate so it doesn't "simplify" a precise word into a vaguer one.

§ The Prompt— fill in the fields, then copy or open in a tool
§ Customize0/4 fields filled
your prompt — fill the fields above
You are a ruthless line editor focused on concision. Tighten the passage below so it reads cleanly and fast. Cut filler, redundancies, hedges, and throat-clearing; merge weak sentences; replace phrases with single strong words. Preserve the original meaning, facts, and [VOICE] tone. Do not add new ideas. Target a [REDUCTION TARGET] cut in word count.

Return: (1) the tightened version, (2) a before/after word count, and (3) a short bulleted list of the biggest cuts you made and why. Keep terminology in [DOMAIN] accurate.

Passage:
[PASTE PASSAGE]
Open with your prompt →ChatGPTClaudeSends your filled-in prompt straight into a new chat.
§ Example Output

What you can expect back

Tightened (43 -> 18 words): Most tenants want one thing above all: a flexible space that adapts as their business grows.

Word count: 43 -> 18 (58% reduction).

Biggest cuts:
- Removed throat-clearing: "In this day and age, it could really be argued that" -> deleted; it adds no information.
- Collapsed hedges: "the vast majority of" -> "Most."
- Killed redundancy: "over the course of time as their business grows and changes" -> "as their business grows" (grows implies change over time).
- Replaced filler phrase: "more than anything else" -> "above all" and moved it for rhythm.

Meaning and the warm, conversational tone are intact; the real-estate sense of 'flexible space' is unchanged.

Illustrative example — your results will vary by tool and inputs.

§ Pro Tips

Get sharper results

  • 01Run it twice: accept the first tightening, then re-feed the result with a smaller target to catch second-pass flab.
  • 02If the model cuts a nuance you needed, restore that one sentence manually rather than loosening the whole reduction target.
  • 03Paste the change log into a personal 'crutch words' list; your padding patterns repeat across every draft.
  • 04For legal or technical copy, lower the target to 15-20% so concision never trades away precision.
§ Variations

Adapt it for your case

Sentence-cap mode

Add 'No sentence may exceed 20 words' to force structural breaks, not just word swaps.

Keep one flourish

Tell it to preserve a single vivid sentence of your choice so the tightened copy still has personality.

Mobile-first

Ask for the version optimized to read on a phone, favoring short paragraphs and front-loaded points.

Use For — Tasks
Tags#concision#editing#self-editing
§ FAQ

Common questions

Will tightening make my writing sound robotic?

No, because you pin a [VOICE] to preserve. Concision removes filler, not personality; if anything reads flat, the model kept a wrong word, so restore that one phrase.

How aggressive a reduction target should I set?

Marketing and web copy tolerate 30-50%; reports and technical docs do better at 15-25%. Start moderate, read the result, then push harder if it still drags.

Can I trust the word counts it reports?

Mostly, but verify with your editor's count for anything published. The numbers are there to keep the model honest, not as a precise audit.

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