Concrete Examples Beat Abstractions: A Rewrite That Adds Real-World Proof

Most thin writing isn’t badly written. It’s just vague. The sentences are grammatical, the grammar is clean, and a reader still finishes the paragraph thinking, “okay, but like what?” That gap between a claim and the proof for it is where good writing lives, and it’s the single easiest thing to fix.

Key takeaways

• Abstract claims ask readers to trust you. Concrete examples let them check. The second one earns more belief with less effort.
• A specific example carries built-in detail — names, numbers, a moment in time — that vague writing can’t fake, which is part of why example-rich prose reads as more human.
• The fastest fix for thin writing isn’t more adjectives. It’s swapping one general sentence for one scene, stat, or story.
• AI-generated drafts lean abstract by default, because the safe, average phrasing is also the vaguest. Adding real proof is where most of the humanizing happens.
• You don’t need an example for every sentence. Anchor each main claim with one and let the rest of the paragraph explain it.

Why abstract writing feels empty

An abstraction is a summary of things you haven’t shown. “Customer service is important” is true, and it’s also forgettable, because the reader has nothing to picture. “Our support team answered a refund request in four minutes at 11 p.m. on a Sunday” is the same idea with a body attached. One asks for agreement. The other gives the reader a reason to agree.

This matters more than it sounds, because readers don’t trust claims by default. They trust evidence. When you write only at the abstract level, you’re essentially saying “believe me,” over and over, and people get tired of that fast. A concrete example flips the burden. Instead of insisting something is true, you show a case of it being true and let the reader draw the obvious conclusion themselves. That conclusion feels like theirs, which is exactly why it sticks.

There’s a second, quieter reason abstraction reads badly. It’s the natural output of writing on autopilot — including AI writing. When a model picks the most probable next phrase, it tends toward the safe, averaged-out middle, and the middle is almost always general. That’s part of why flat, average writing trips detectors: predictable, low-surprise text looks statistically smooth. Specifics are surprising. A real number or an oddly precise detail is the kind of thing a generic sentence would never have produced.

The rewrite: before and after

Here’s a paragraph in its first-draft, all-abstraction form. It’s the kind of thing you’d get from a quick AI draft or a tired writer.

> Procrastination is a common problem for students. It can lead to stress and lower-quality work. Managing your time better can help you avoid these negative outcomes and improve your academic performance.

Nothing there is wrong. It’s also completely weightless — every sentence is a category, not a thing. You could delete it and lose nothing, because the reader already knew all of it.

Now the same point, anchored to something concrete:

> Procrastination wrecks the work, not just the schedule. Picture a student who starts a 2,000-word essay at 9 p.m. the night before it’s due. By midnight she’s not writing arguments anymore; she’s padding sentences to hit the word count. The thesis she half-formed at 11 never gets the second draft that would’ve sharpened it. That’s the real cost — not the late night, but the version of the essay that never got written. A simple fix, like blocking two 45-minute sessions earlier in the week, exists mostly to protect that second draft.

Same claim. But now there’s a person, a clock, a word count, and a specific failure mode (padding instead of arguing). The reader can see it. And notice the rewrite is barely longer than the original — concreteness isn’t about adding words, it’s about spending them on something real.

What changed, mechanically

Three swaps did most of the work, and you can copy them:

• A category became a case. “Students” became “a student starting a 2,000-word essay at 9 p.m.” One imagined person beats a whole demographic.
• An effect became a mechanism. “Lower-quality work” became “padding sentences instead of writing arguments.” Showing *how* it goes wrong is more convincing than naming that it goes wrong.
• Advice became a reason. “Manage your time better” became “block two sessions to protect the second draft.” The example gave the advice a job to do.

Where to add examples (and where not to)

You don’t carpet a piece with examples. You place them. The rule that holds up: put a concrete example right after any claim a reasonable reader might doubt, and in any spot where the writing feels vague even to you.

That second test is underrated. When you reread a draft and a sentence makes you a little uneasy — like it could’ve been written by someone who never actually did the thing — that discomfort is a flag. It almost always means you reached for an abstraction because you didn’t want to commit to a specific. Commit. The specific is usually right there in your memory; you just skipped it.

A quick mini-scenario

Say you’re writing a paragraph for a SaaS blog and you’ve got this line: “Our onboarding is designed to reduce friction for new users.” A skeptic reads that and hears marketing noise. So you ask one question — *what’s an actual friction we removed?* — and the answer is: we cut the signup form from eleven fields to three. Now the line becomes: “We cut our signup form from eleven fields to three, so a new user is in the product before they lose interest.” The claim didn’t change. The proof showed up. And “eleven to three” is the kind of detail no autopilot draft would invent, which is exactly why it reads as written by a person who was there.

Where *not* to add examples: places where the reader already agrees, or transitions that just need to move the piece along. If you example-stack a point nobody’s contesting, you’re spending the reader’s attention on convincing them of something they already believe.

How this fits into humanizing AI drafts

If you’re cleaning up text from a model, this is often the highest-leverage edit you can make, and it’s worth understanding why. A raw AI draft tends to be fluent, organized, and strangely hollow — lots of well-formed claims, very little lived detail. The fix isn’t to scramble the words or swap in thesaurus synonyms. It’s to go claim by claim and ask the same question every time: *what’s one real, specific thing that proves this?*

Doing that does two jobs at once. It makes the piece more convincing to a human, which is the whole point. And it raises the specificity and variation in the text, which happens to be part of what detectors are reading when they estimate how machine-like something looks. You’re not gaming anything — you’re writing the version that should’ve existed in the first place.

One honest caveat. Adding examples is a craft move, not a magic verdict-flipper. Detectors deal in probabilities, and there’s no technique guaranteed to change a score. Treat the score as feedback on flatness, not as the goal. For more on the writing side of this, there’s a whole shelf of writing-craft guides worth browsing.

A simple workflow you can run

When a draft feels generic, do this in one pass:

1. Highlight every sentence that’s a claim. Anything asserting something is true, good, common, or important.
2. For each, ask: what’s one concrete thing behind this? A number, a name, a moment, a step, or an honest hypothetical you label as one.
3. Replace or follow the claim with that thing. Don’t delete the claim — let the example serve it.
4. Cut the duplicates. If two sentences now make the same point, keep the one with the example.

Run that on three paragraphs and you’ll feel the difference immediately. The writing stops sounding like it’s describing the world from a distance and starts sounding like it’s been somewhere.

The bottom line

Abstraction is the default setting of tired writing and machine writing alike, because the vaguest version is also the easiest one to produce. Concrete examples are the antidote: they give the reader something to see, they earn belief instead of demanding it, and they fill in the specific texture that generic prose can’t fake. The skill isn’t fancy. It’s just the discipline of answering “like what?” every time you make a claim.

If you’ve got an AI draft that reads smooth but hollow, that’s usually a specificity problem hiding as a style problem. Run it through PaperBleach to spot the flat stretches, then anchor each one with something real.