The Problem with “Perfect” AI Writing—and How to Avoid Generic AI Writing

There’s nothing quite like hitting “generate” and watching a dozen AI-assisted drafts roll in. Every one of them is neatly formatted, free from typos, and structured like something pulled from the front page of a business newsletter. If you use AI for content, you’ve seen the magic and the slop. That first hit of speed. The instant relief from blinking cursors and blank pages.

But the further I read, the more a flatness set in. These drafts technically worked. Every sentence was correct. Yet every piece, no matter the topic or prompt, reminded me to avoid generic AI writing—it all felt dipped in the same vanilla coating. That’s when it hit me—technical polish is just the starting line. If you actually care about making something people want to read, polish alone is a trap, not a finish line.

Suddenly, it’s like the dam bursts. Your thoughts finally spill onto the page, dozens of posts, instantly. At first, it’s amazing. Then you notice something off.

But as I waded through the drafts, a weird discomfort crept in. They weren’t bad, but they weren’t me. I could feel my own ideas getting washed out by something I didn’t quite recognize.

It’s always one of two culprits. Some drafts sound like a generic LinkedIn influencer, declaring bland truths with maximum confidence. Others have the careful manners of a polite robot, overly formal and trying too hard to offend no one. It’s like getting advice from someone wearing a mask. You hear the words, but not the person. If you’ve shipped AI-generated docs or release notes, you know this voice. It’s everywhere, and it’s forgettable.

Here’s the promise: you can absolutely keep that speed, inject your own voice, and make it stick—across posts, docs, release notes, whatever you ship next. This is just the beginning.

Why AI Outputs Feel Generic—and Why Your Voice Matters

Here’s the blunt truth. Large language models are built to play it safe. They predict the most probable next word, not the most interesting one. So when you ask AI to fill a page, it fills fast, but it fills it with filler. That filler vibe comes straight from how these models pick one of the highest-probability words next—always leaning toward what’s most expected, not what’s most distinct (read). AI can churn out something that’s grammatically perfect on the first try, but it’s just assembling patterns it’s seen a million times before. It’s average by design.

This is why technical polish is table stakes now. Anyone can get it with a free prompt and a few minutes. What’s actually valuable is the part only you bring: your specific voice, your take, your story. The latest Google updates push human-first writing up the rankings by rewarding teams that humanize AI content—so if you’re only chasing correct phrasing, you’re missing what actually works for real people (see). If you want work that stands out, you can’t skip this step. Shape the output until it feels like something only you would hit publish on.

It’s weirdly easy to spot when an AI draft slides sideways into this corporate-memo tone. Suddenly you’re reading lines like, “In today’s rapidly evolving landscape…” or “Let’s unlock the power of transformation!”—phrases no real human has ever said out loud at their desk. Other times, the draft gets chirpy and hollow, full of empty “Let’s crush it!” pep without substance. That ultra-agreeable, bland tone? It’s baked in. There’s even a name for it—a documented model habit called sycophancy, where the system smooths things over by just mirroring you instead of pushing forward (source). I catch myself deleting it all the time.

I get the appeal. Fast, neat, and error-free is seductive, especially if you’re working under a deadline. But “correct” without character gets lost in the flood. Back when I first started automating drafts, I kept convincing myself it was good enough if it just sounded clean. You already know how quickly AI can blast out a hundred posts. The real edge is making even one sound like you.

How to Edit AI Content So It Actually Sounds Like You

Here’s the workflow I use every single time, no matter what I’m drafting. First, let the AI do its thing. Get your messy first draft out in seconds. Celebrate the speed, but don’t mistake it for done.

The real magic kicks in when you sit down to edit, ruthlessly deleting anything that smells like filler or reads like it’s trying too hard to fit in. Instead, I add specifics. Details pulled straight from my experience, phrases I actually use with clients, stories from bugs or releases I just lived through. The shift is simple, but profound. You’re the difference between “just content” and content people remember. This extra layer isn’t a luxury; it’s the whole point. If you want your writing to stick, you have to shape it beyond the AI’s comfort zone—until it sounds unmistakably like you.

Start by reading your draft out loud, either to yourself or literally into your phone. It’s the fastest way to catch tone mismatches, robotic phrasing, and those influencer clichés that sneak in. “Unlock your team’s potential.” “In today’s landscape.” You know the lines. When you hit a sentence that rings false or makes you cringe, stop and rewrite it. I do this on every pass. It’s the single best trick I’ve found for cutting generic fluff.

Here’s where you really crank up the authenticity: drop in details only you could know. Mention a recent release (“Shipped v2.1 last week and broke the onboarding UX for half our users”), reference a bug report (“the infamous WidgetNotFound exception”), or quote a customer who called your product “a surprisingly friendly black box.” Each thread of specificity pins your writing to reality and makes everything else more believable.

Editing out filler is a lot like linting a codebase or finally cleaning out the junk drawer in your kitchen. It’s oddly satisfying, watching the unnecessary parts disappear—leaving only what matters. Sometimes I don’t realize how much sludge there is until I’ve taken a hard look. Whether I’m deleting “strategic solutioning” from a release note or straightening out a rambly intro, cutting out the nonsense always leaves the essentials shining through.

You’ll get sharper the more you do this. I keep a living “voice file” open—a running doc of phrases I actually say, lines from old posts that feel right, and a blacklist of canned language I refuse to use again. The more I analyzed my drafts, the clearer my tells became. It’s weirdly comforting to see your own patterns in print.

Finally, layer in constraints for consistency. Decide on your preferred structure. Always start with the problem, add an example, use numbered lists sparingly. Set standard headings, and even choose the kind of examples you want to stick to—real bugs, actual quotes, hard numbers. That way, every piece feels unified, but never identical. Over time, this method lets you ship work that’s both uniquely yours and reliably strong, giving readers a sense of trust with each post you release.

Scaling Your Voice Across Every Format

When it comes to posts, there’s one trick that consistently works: start with a story you actually lived and a take you really believe—no matter how rough around the edges. That’s what readers remember. Drop the “thought leadership” cadence unless you actually speak like that at lunch. If you catch yourself sliding into the rhythm of a motivational thread or LinkedIn pep talk (“Here are seven ways…”), stop and reroute. For me, it’s usually about asking, “Would I talk about this in this way with a peer?” If not, I rewrite until it matches my own pace—sometimes choppy, sometimes direct, never generic.

Documentation is where sterile language creeps in the fastest. Here, specifics are your secret weapon. Call out the actual variable names, include the real commit IDs, and anchor explanations with the tradeoffs you wrestled with. Write every section as if you’re leaving trail markers for a previous version of yourself who hit the same wall. The more you “show your work,” the easier it is for readers—and future-you—to build on it.

Release notes are the most tempting place to get vague, so spell out specifics to avoid generic AI writing. “General improvements” or “Performance enhancements” mean almost nothing. Instead, ground every line in the reality of what changed, when, and why. Yesterday: “Fixed WidgetNotFound crash on user signup.” This week: “API latency dropped by 120ms—users should see faster dashboard loads.” Give customers a map they can follow. The callback here is simple: clear specifics build trust over time, while generic blur kills attention. That voice I was fighting at the start? Here’s where you can put it to work for you—for the entire team.

Quick example: Replace “We’re always improving performance for our users” (which could have come from anyone, anywhere) with “Cut average dashboard load time by half after chasing a memory leak for two sprints—if you see one more, hit me up.” It’s a sentence only I would write, and it shows I was actually there. Sometimes, admitting you were stumped before you got it right? That’s what makes readers trust you.

Addressing Time, Voice, Usefulness, and Consistency

Editing AI-assisted drafts almost always feels slow at first—I won’t sugarcoat that. You crack open your “finished” post, spot a dozen lines of filler, and know you have to dig in. But here’s the part that flipped my workflow. Clearing out the bland stuff early means I spend way less time on fit-and-finish revisions later. It isn’t just a slog up front. It actually cuts the revision loop and speeds up publishing across the whole week. The bonus is framing cuts down back-and-forth, which stabilizes outputs and keeps your energy up.

Let’s talk voice for a second. You do have one, even if you doubt it. Proof is everywhere—in your Slack threads, bug comments, how you write up demos for the team. Scrape your last ten commit messages or chat logs and you’ll hear it loud and clear. Collect a few, lay them out, and start listening for patterns. It makes the difference obvious.

People worry that if they get specific (“last Wednesday, we cut 93ms from latency targeting SFO users”) the post won’t be usable for others. Honest truth: going specific doesn’t narrow the value; it sharpens the pattern. A weird detail isn’t a barrier—it’s a shortcut for others who need the same fix and can swap in their own context. Lessons travel better when you show your work.

Here’s how I keep consistency from turning into monotony. Set a repeatable structure, choose the type of examples, and pin your preferred tone. Guardrails keep every piece sounding like you when you prime AI with style, even as the details change. Once you’ve set those markers, publish—it gets sharper every time. Technical polish is just the starting line now.

One thing I still haven’t totally worked out: sometimes, I’ll hit publish on a post and spot one “robotic” phrase I missed a day or two later. It nags at me, but I leave it up. Maybe that bit of leftover grit is part of what keeps the process honest.