Introduction

Sometimes the fastest way to understand bad AI writing is to watch it get rewritten.

The Humanizer skill is full of useful before-and-after examples. This post turns that approach into a practical swipe file you can use while editing.

1. Fake significance

Before: "The update marked a pivotal moment in the evolution of team collaboration."

After: "The update moved comments and approvals into one place."

2. Vague notability

Before: "Her work was covered by major media outlets around the world."

After: "In a New York Times interview, she argued for outcome-based AI regulation."

3. Superficial -ing phrase

Before: "The feature improves handoff speed, ensuring smoother collaboration across teams."

After: "The feature keeps handoffs in one queue, so the team does not lose track of approvals."

4. Promotional wording

Before: "This vibrant dashboard offers a groundbreaking experience for modern teams."

After: "This dashboard shows open tasks, blocked items, and recent approvals."

5. Vague attribution

Before: "Experts say the workflow is critical for performance."

After: "The workflow removes two approval steps before publishing."

6. Outline-style challenge section

Before: "Despite these challenges, the company continues to thrive."

After: "The company cut support backlog after moving billing issues into a separate queue."

7. AI vocabulary

Before: "Additionally, the platform showcases a pivotal feature set."

After: "The platform also includes version history and inline comments."

8. Copula avoidance

Before: "The tool serves as a central hub for campaign planning."

After: "The tool is the team's campaign planning workspace."

9. Negative parallelism

Before: "It is not just about speed, it is about trust."

After: "Faster replies matter because customers stop wondering who owns the request."

10. Rule of three

Before: "The workshop offers insight, inspiration, and connection."

After: "The workshop includes talks, open Q&A, and time to meet people afterward."

11. Synonym cycling

Before: "The founder, the entrepreneur, and the business leader all made the same point."

After: "The founder made the same point throughout the interview."

12. False range

Before: "The guide covers everything from ideation to transformation."

After: "The guide covers research, outlining, drafting, and editing."

13. Subjectless fragment

Before: "No setup needed. Results saved automatically."

After: "You do not need to set anything up. The system saves the results automatically."

14. Filler phrase

Before: "In order to improve clarity, it is important to note that shorter intros perform better."

After: "To improve clarity, shorter intros usually work better."

15. Generic happy ending

Before: "The future looks bright as the brand continues its journey toward excellence."

After: "The brand plans to launch two more product lines this year."

How to use this list

Do not treat these as one-off sentence swaps. Treat them as pattern detectors.

When a paragraph sounds AI-generated, ask which bucket it belongs to. Is it padded with filler? Is it using fake importance language? Is it hedging, flattering, or generalizing when it should be naming a fact?

Once you know the bucket, the edit usually gets easier.

If you want a full process around that, use How to humanize AI text without changing meaning and the humanize AI text checklist.

Conclusion

Good AI editing is rarely about one magic prompt. It is about noticing the recurring habits that make generated text feel staged.

The more examples you see, the easier it gets to fix them on sight.

Before and after: 15 AI writing fixes that actually work