Building a Reusable Bilingual Chinese-English Skill for Removing AI-Generated Writing Patterns

As large language models are increasingly used for content production and code assistance, a persistent problem is that model-generated text often carries a recognizable “AI flavor”: sentences are too evenly structured, transition words are stacked, and conclusions tend to be generic. Such writing can reduce credibility and readability in technical documentation, marketing copy, email responses, and similar contexts. This article does not focus on a specific tool. Instead, it starts from writing patterns to explain how AI flavor emerges, what traces can be recognized, and how those experiences can be abstracted into a structured set of rules. These rules can then be packaged as a reusable, testable, bilingual Chinese-English “de-flavoring” skill. This approach can support manual editing and also serve as a reference for prompt and workflow design in intelligent coding tools or content platforms.

1. What Is “AI Flavor,” and Why Does It Show?

“AI flavor” is not a precise technical metric. It is an intuitive judgment readers make while reading: the text lacks personal style, has low information density, shows artificial emotional expression, and follows predictable structure. Its root cause lies in how language models work. Models tend to output expressions that are statistically “safest” and most generic, so their vocabulary and syntax show strong statistical regularities.

Put differently, AI flavor is essentially a product of “central-tendency” generation: models repeatedly choose high-frequency transition words, templated openings, parallel structures, and universal conclusions. When these features co-occur frequently in a passage, readers’ experience triggers the judgment that “a machine wrote this.”

Understanding this point is important: removing AI flavor does not mean making text more elaborate. It means breaking predictability and bringing the text back to the natural variation range expected in human writing.

2. Common Traces of AI-Generated Text

To build a rule library, one must first recognize the traces. The following patterns recur in LLM-generated text and are the main source of de-flavoring rules:

• High-frequency boilerplate and universal sentence patterns: “first/second/finally,” “in summary,” “in today’s rapidly developing era,” “it is worth noting that,” “not just… but also…”
• Excessive parallelism and three-part structures: sentences have highly similar length and structure, lacking the rhythm of mixed long and short sentences.
• Vague value elevation: endings are forced into grand conclusions with meaning or outlooks lacking concrete support.
• Semantically safe but low-information content: many descriptive adjectives, but few specific numbers, cases, or scenarios.
• Overuse of em dashes and parenthetical insertions: too many explanatory interjections interrupt the reading rhythm.
• Templated emotional expression: emotion is stacked with phrases such as “makes people feel warm” or “brings a brand-new experience” without detail.
• Overly regular structure: paragraphs have similar lengths, each paragraph begins with a topic sentence, and each paragraph ends with a summarizing sentence.

Individually, these traces are not fatal, but their high-frequency co-occurrence creates a clear “machine fingerprint.”

3. Abstracting Writing Patterns into Executable Principles

Recognizing traces is only the first step. What can be implemented is abstracting experience into principles. The following core de-flavoring principles are distilled from the traces above and can serve as the skeleton of a rule library:

1. Information first: remove vague modifiers, and make each claim land on concrete facts, numbers, or scenarios whenever possible.
2. Rhythm variation: actively break up sentence patterns; alternate long and short sentences; avoid structural uniformity.
3. Restraint in transition words: use semantic connection instead of mechanical connectors; remove transition phrases that can be inferred from context.
4. De-template openings and endings: avoid boilerplate openings and elevated closings; let conclusions emerge naturally.
5. Concrete over abstract: change “significantly improve efficiency” into verifiable descriptions.
6. Preserve necessary “imperfection”: moderate personification, pauses, and colloquial expressions can improve authenticity.

The value of these principles is that each can be turned into executable instructions for a model or a manual checklist for human review.

4. Designing a Bilingual Chinese-English De-Fluffing Skill

A “Skill” here refers to a packaged, reusable capability module. It usually consists of three parts: a system prompt defining the role and constraints, a rule library containing executable rules, and a self-check checklist for post-output verification. Its goal is not to replace writing, but to inject stable de-flavoring constraints into generation or editing steps.

4.1 Three-Layer Structure of the Skill Package

• Role layer: tell the model who it is, who the target readers are, and what qualifies as acceptable text.
• Rule layer: translate the principles in Section 3 into specific executable constraint items.
• Verification layer: have the model check against a checklist before output to reduce missed revisions.

4.2 Layered Design of the Rule Library

It is advisable to divide rules into “hard constraints” that must be satisfied and “style preferences” that should be satisfied where possible. This avoids over-editing caused by treating all rules equally. A schematic configuration is as follows:

skill: de_ai_flavor
languages: [zh, en]
rules:
  hard:
    - id: remove_fillers
      desc: Remove non-informational boilerplate such as "in summary" and "in today's era"
    - id: add_specificity
      desc: Replace vague claims with specific data, cases, or scenarios
    - id: limit_hedging
      desc: Reduce redundant qualifiers such as "may," "perhaps," and "to some extent"
  soft:
    - id: vary_sentence_length
      desc: Alternate long and short sentences to avoid uniform structure
    - id: cut_em_dashes
      desc: Reduce em dashes and parenthetical insertions
    - id: natural_tone
      desc: Preserve moderate natural tone in formal contexts

4.3 Differentiated Handling for Chinese and English

The manifestations of AI flavor in Chinese and English are not exactly the same, so rules need to distinguish them:

• Chinese focus: reduce “first/second” enumerations, remove excessive four-character idioms, and avoid empty sublimation.
• English focus: reduce high-frequency phrases such as “It is important to note that,” “delve into,” and “in today’s fast-paced world,” and reduce overuse of em dashes and the “not just X but Y” structure.

Condition-based matching can be done in the same rule library through a lang field, for example:

{
  "id": "cut_openers",
  "lang": "en",
  "banned": ["It is worth noting that", "delve into", "in today's world"],
  "action": "rewrite_or_remove"
}

5. Sample System Prompt

Below is a system prompt framework that can be embedded directly into intelligent coding tools or content platform workflows:

You are a senior editor responsible for removing the "AI flavor" from text
while preserving the original meaning and professionalism.
Strictly follow these rules:
1. Delete non-informational boilerplate and universal sentence patterns.
2. Replace abstract claims with concrete facts, data, or scenarios.
3. Break up uniform sentence patterns; alternate long and short sentences;
   reduce mechanical transition words.
4. Do not use templated openings or elevated conclusions.
5. Before output, check against the self-verification list to confirm nothing is missed.
When the input language is Chinese or English, apply the corresponding language rule set.
Output only the edited text; do not add explanations.

6. Validation and Evaluation

The effect of de-flavoring must be measurable; otherwise, it easily falls into subjective judgment. The following methods can be used:

• Checklist self-check: have the model report rule hits one by one and quantify the number of revisions.
• A/B comparison: conduct blind human evaluation on the original and de-flavored text, assessing naturalness and credibility.
• Regression testing: use a fixed set of “high AI flavor” samples as a baseline to ensure rule updates remain stable.
• Avoid over-editing: set a “minimum change” constraint to prevent technical accuracy from being altered.

It must be emphasized that de-flavoring must not come at the cost of factual accuracy or professional terminology. The verification step should retain a hard constraint that facts must not be changed.

7. Limitations and Continuous Optimization

A de-flavoring skill is not a universal solution. It cannot supply missing real information or viewpoints out of thin air; it can only optimize expression. For highly templated official documents or legal texts, excessive de-flavoring may become non-compliant. Therefore, the rule library should be treated as an iterable product: continuously add new high-frequency traces based on feedback, adjust the ratio of hard and soft rules, and expand the bilingual phrase library.

In summary, turning scattered writing experience into structured rules and a reusable capability package is an engineering approach. It changes “removing AI flavor” from an occasional behavior dependent on individual experience into a testable, reusable, and continuously optimizable process capability. Whether used for content creation or integrated into assisted writing scenarios in intelligent coding tools, this method can significantly improve the naturalness and readability of generated text.