Why AI Writing Sounds Fake (And How to Fix It With the writ Skill)
You can spot AI-generated prose within three sentences.
It rarely stems from grammatical errors. Large language models understand syntax and subject-verb agreement better than most human writers. The tell is subtler: an unearned earnestness, a predictable rhythm, and an insistence on explaining why everything it mentions is historic, essential, or transformative.
When readers encounter this tone, they stop reading. Search engines downrank it. Communities reject it.
Over the past two years, Wikipedia editors cataloged thousands of AI-generated article submissions to isolate exactly what gives synthetic prose away. They compiled their findings into a guide called Signs of AI Writing.
To turn those observations into an automated, actionable workflow for developers and writers, I built writ—an open-source AI self-audit skill that systematically strips synthetic habits out of machine drafts.
Here is an analysis of why language models write this way, the specific patterns that give them away, and how to audit your text before publishing.
The Core Problem: Over-Narrated Significance
The single biggest tell in AI text is not a specific word. It is the impulse to explain the importance of a statement instead of simply stating it.
Human writers trust their audience. If you report that an engineering team migrated from PostgreSQL to ClickHouse and cut query latency by 80%, the reader grasps the impact immediately.
A standard language model cannot resist adding a participatory tail:
"...slashing query latency by 80%, highlighting the team's commitment to efficiency and underscoring the transformative shift toward real-time analytics."
Notice what happened. The sentence stopped delivering information and started narrating its own importance. If you delete that final clause, the factual value remains unchanged.
Language models behave this way because reinforcement learning from human feedback (RLHF) rewards outputs that sound complete, polite, and authoritative. In practice, that training creates prose that sounds like an anxious corporate press release.
The Six Most Common AI Writing Tells
The writ skill codifies Wikipedia's catalog into a targeted checklist. When auditing drafts, these six patterns appear most frequently:
1. Dangling Participial Clauses
Sentences that end with trailing -ing clauses designed to manufacture weight:
- The tell: "...marking a pivotal moment," "...serving as a testament to," "...highlighting the growing importance of."
- The fix: Cut the clause. State the fact and place a period at the end of the thought.
2. Stock Significance Vocabulary
Certain words are fine in moderation, but language models cluster them with statistical regularity:
- Banned cluster: delve, robust, tapestry, realm, underscore, leverage, foster, holistic, invaluable, seamless, game-changer, cutting-edge, testament, pivotal.
- The fix: Replace abstract descriptors with precise nouns and verbs. Instead of "a robust data pipeline," write "a pipeline handling 50,000 events per second."
3. The False Contrast Habit ("It's not X, it's Y")
Language models frequently reach for rhetorical negative parallelism to fake depth:
- The tell: "It's not just a database—it's the backbone of modern data infrastructure."
- The fix: Use contrast only when two concrete alternatives are genuinely in conflict. Avoid using it as decorative phrasing.
4. Em Dash Overuse
AI models lean heavily on em dashes (—) to splice thoughts together where human writers naturally use commas, colons, parentheses, or separate sentences.
- The fix: Limit em dashes to sharp, deliberate interruptions. When in doubt, break the thought into two shorter sentences.
5. Defensive Hedging and Fabricated Debates
Models often invent controversy around mundane facts to appear objective:
- The tell: "While opinions vary on the exact year Python was conceived..."
- The fix: Python was released in 1991. Settled technical facts do not require diplomatic hedging.
6. Rhythmic Uniformity
AI prose tends to settle into a metronomic cadence: sentence of twelve words, followed by sentence of fourteen words, followed by sentence of thirteen words.
- The fix: Introduce variation. Mix short fragments with complex explanations. Break the rhythm intentionally.
What is the writ Skill?
writ is an open-source, prompt-based self-audit skill designed for AI coding agents, developers, and technical writers.
Instead of generating text from scratch, writ acts as an editorial second pass. It ingests an existing draft, scans the text against the codified Wikipedia heuristics, identifies synthetic patterns, and rewrites the passage while preserving the author's technical intent and data points.
┌──────────────────────┐
│ Raw Draft / AI Output│
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ writ Audit Engine │
│ • Kill tacked clauses│
│ • Strip stock terms │
│ • Break cadence lock│
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ Natural Human Prose │
└──────────────────────┘
Before and After: The Audit in Action
To understand how writ works, examine this comparison taken from a technical system overview:
The Raw AI Output:
"In today's fast-paced technological landscape, caching plays a vital role in backend architecture. By seamlessly storing frequently accessed records in memory, Redis acts as a game-changer for distributed systems, underscoring the critical need for low-latency operations and highlighting the shift toward real-time responsiveness."
Notice the symptoms:
- "In today's fast-paced technological landscape" (cliché opener)
- "plays a vital role" (stock significance)
- "seamlessly... game-changer" (unearned hyperbole)
- "underscoring the critical need... highlighting the shift" (double dangling participial clause)
After Running Through writ:
"Redis stores hot keys in memory to keep read latencies under two milliseconds. For high-throughput services, that eliminates repetitive queries against the primary database and prevents connection exhaustion during traffic spikes."
The edited version is shorter, contains specific technical metrics, and provides concrete engineering rationale without posturing.
How to Use writ in Your Workflow
The repository is hosted on GitHub at github.com/Avinashricky211/writ. You can integrate it in three ways:
Method 1: Using it in Agentic Coding Tools (Antigravity, Cursor, Claude Code)
Clone or add the writ directory directly into your agent skills path:
git clone https://github.com/Avinashricky211/writ.git
When prompt-instructing your agent, invoke the skill:
Review this article draft using /writ. Strip all synthetic AI tells, remove dangling significance clauses, and ensure the tone reads as a technical peer speaking to another engineer.
Method 2: Manual Terminal / Prompt Pass
You can copy SKILL.md from the repository and paste it into your system prompt when generating or revising blog posts, release notes, documentation, or newsletters.
Method 3: Pre-Commit CI Documentation Check
Add writ as an automated review step in your CI documentation pipelines to flag PR descriptions and markdown docs that lean too heavily on marketing adjectives.
Key Takeaways for Technical Writers
- Facts carry their own weight: If a technical milestone is impressive, detailing the benchmark numbers will convince the reader. Describing it as "groundbreaking" will not.
- Read your sentences aloud: If every sentence shares the exact same length and structure, your ear will catch what your eyes missed.
- Audit after drafting: Write your rough draft without self-censorship, then run a systematic audit specifically hunting for stock vocabulary and participial clauses.
You can inspect the rules, contribute heuristics, or download the full skill at github.com/Avinashricky211/writ.
Written by **Yadlapalli Avinash Ricky, AI Engineer, Author of The Art of AI Prompts, and creator of AviGPT-250M.
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