AI Prompts for Improving Readability: A Case Study

Rewrite this to a Grade 6 reading level. [PASTE MEDICAL TEXT]

Does a better readability score actually mean better writing? It's a fair question, because chasing that number is one of the easiest ways to make your writing worse. Readability formulas mostly count syllables and sentence length, so you can "improve" a score by chopping every sentence into choppy fragments and swapping precise words for vague short ones — and end up with something that scores well and reads like a robot. This case study shows how ai prompts for readability improvement work when you aim them at real clarity instead of gaming a metric.

The Problem the Writer Faced

Meet the writer: a healthcare content specialist writing patient education materials that legally had to hit a specific reading level — around Grade 6, because that's who her readers were. Her drafts kept scoring at Grade 11. Real people were struggling to understand important medical instructions, which was a genuine problem, not a vanity metric.

Her challenge was sharp: simplify medical content to Grade 6 without making it wrong, condescending, or robotic. Health information has to stay accurate even as it gets simpler, which is a harder balance than most readability work.

The Wrong Approach

Her first attempt chased the number directly:

Rewrite this to a Grade 6 reading level. [PASTE MEDICAL TEXT]

The score improved. The writing got worse. The model chopped everything into staccato three-word sentences that read like a children's book and, worse, occasionally lost the medical nuance — "take with food" became "eat first," which isn't quite the same instruction. It hit Grade 6 by dumbing down rather than clarifying, and in medical content, that's dangerous.

⚠️ Common mistake: Prompting for a target reading level as the goal. "Make this Grade 6" tells the model to optimize the score, which it does by shortening and simplifying blindly — producing choppy, sometimes inaccurate text. The score is a proxy for clarity, not clarity itself. When you optimize the proxy directly, you often destroy the thing it was measuring.

The Correct Prompt

The fix was to target the causes of poor readability, not the score:

This patient material needs to be clear for readers with limited health literacy. Don't chop it into fragments or dumb it down. Instead: - Replace jargon with plain equivalents, but keep every medical fact exact. - Break only the genuinely overloaded sentences (two ideas crammed into one). - Flag anything I've simplified so much it's now slightly inaccurate. Preserve accuracy above all. [TEXT]

What this does: It fixes the real drivers of low readability — jargon and overloaded sentences — while explicitly protecting accuracy, so the writing gets genuinely clearer instead of just scoring better. The accuracy flag catches dangerous oversimplification.

Then, a targeted diagnostic:

Don't rewrite. Point to the 5 specific things making this hardest to read — name each one (a jargon term, a 40-word sentence, a double negative) so I can fix them myself and learn the pattern.

What this does: It teaches her what actually hurts readability in her own writing, so she writes clearer first drafts over time instead of depending on a rewrite pass forever.

Results and What Changed

Her materials hit Grade 6 — but by genuine clarity, not fragmentation. Jargon became plain words with the facts intact. Overloaded sentences got split where they were actually overloaded, not everywhere. Patients understood the instructions. And because the accuracy flag caught the "eat first" type of drift, nothing important got lost in translation. The score followed the clarity, which is the correct order.

She also got faster at first drafts, because the diagnostic taught her which habits — a few pet jargon words, a tendency to cram two instructions into one sentence — were tanking her readability in the first place.

⚡ Pro tip: Readability is mostly about two things: word familiarity and sentence load. Fix those directly — swap unfamiliar words for common ones, and split sentences that carry two ideas — and the score improves as a side effect. Ask the model to target causes, never the number. Optimizing the cause fixes the writing; optimizing the number fixes only the writing.

⚡ Pro tip: Short sentences aren't automatically clear, and long ones aren't automatically hard. A 25-word sentence with a clear structure reads easier than three choppy fragments a reader has to reassemble. Ask the model whether a long sentence is hard because of length or because of structure — often it's structure, and restructuring beats chopping.

How to Apply This to Your Situation

The target-the-cause approach works anywhere clarity matters. A UX writer simplifying error messages targets jargon and ambiguity, not a score. A technical writer making documentation accessible splits genuinely overloaded steps rather than shortening everything uniformly. A lawyer drafting plain-language contracts replaces legalese with plain terms while a flag protects the precise legal meaning that has to survive.

The principle throughout: readability scores are a useful check, never a target. Aim at the real causes of confusion, protect accuracy, and let the number follow.

⚡ Pro tip: Read anything you've simplified aloud to check it didn't become condescending. Over-simplified writing has a sing-song, talking-down quality that alienates readers even when the score is great. Ask the model to flag any passage that sounds like it's talking down to the reader — respect and clarity aren't opposites.

A Prompt for Checking You Didn't Lose Meaning

The real danger in any simplification pass is silent meaning-loss — text that reads clearer but now says something subtly different. This catch-prompt guards against it:

Here's my original and my simplified version. Don't judge the writing. Tell me only: is there any place where the simplified version changed, softened, or dropped a fact, instruction, or caveat from the original? List every difference in meaning.

What this does: It isolates meaning-changes from style-changes, so you can confirm your clearer version is still accurate — critical in medical, legal, or safety content where a dropped caveat is dangerous.

⚡ Pro tip: Run this meaning-check on anything where being wrong has consequences. Clarity that introduces an error isn't clarity — it's a clearer mistake. Always diff the simplified version against the original for lost facts before you ship, especially when you simplified aggressively to hit a target.

Next Steps

Take a piece that's scoring too hard to read and run the cause-diagnostic prompt: what are the five specific things making it difficult? Fix those yourself, protect your accuracy, and check the score last. You'll get writing that's genuinely clearer, not just numerically compliant.

The cause-targeting rewrite and the readability diagnostic are tools for anything that has to be understood the first time — patient materials, documentation, contracts, onboarding. Save your readability set in PromptABCD, and the next time a score comes back too high, you'll fix the real problem instead of chopping your writing into fragments to satisfy a formula.