Removing the "AI Feel" from Text: A Practical Approach for Chinese and English Content

1. What “AI Feel” Actually Means

“AI feel” is not a matter of luck or intuition. It refers to a set of observable, describable writing preferences that large language models pick up through statistical learning and alignment training. Once you read a few model-generated passages in a row, you start to get a feeling: the text reads smoothly, yet something about it seems off. The surface features behind that impression are easy to break down. They include frequent formulaic transitions, overly symmetrical parallel structures, an evenly neutral tone, and vague phrasing that says little of substance.

Knowing how to spot these features has practical value for developers and content teams. The “AI feel” of a piece affects its credibility and professional polish. It also directly shapes the quality of the content that end users see. The aim of this article is to turn de-slopping from a vague gut feeling into a practical, repeatable engineering method.

2. AI Writing Markers in Chinese and English: A Side-by-Side Checklist

The first step toward cleaner text is a checklist to compare drafts against. Chinese and English text share some markers of AI writing, and each language also has its own habits.

Chinese text

• Overused transitions: phrases such as 首先/其次/最后 (first/second/finally), 总而言之 (in summary), 综上所述 (in summary), 值得注意的是 (it is worth noting) and 不难发现 (it is easy to see) appear constantly, as if every paragraph has to bridge the point before it to the point after.
• Parallelism and three-part structures: any point is likely to be split into three symmetric parts. The result reads neatly but has no clear focus.
• Negation-and-pivot templates: constructions such as 不仅仅是……更是…… (not merely …, but more importantly …) and 与其说……不如说…… (rather than …, it is better to say …) keep reappearing.
• Overly neutral tone: the writing takes no stance, shows no emotion and offers no personal perspective. It sounds like a manual that is never wrong about anything.
• Piles of abstract nouns: the page is full of terms like 赋能 (empower), 闭环 (closed loop), 生态 (ecosystem) and 维度 (dimension), but real people, events and data are scarce.

English text

• Signature vocabulary: phrases such as “delve into”, “it’s important to note”, “in today’s fast-paced landscape”, “a testament to” and “navigate the realm of”.
• Formulaic closers: “In conclusion”, “Overall” and “To summarize” introduce a closing paragraph that could be deleted without any loss.
• Over-balancing: constructions such as “not just X, but Y” and “it’s not about A, it’s about B” turn up everywhere.
• Stacked adjectives: words with a marketing sheen, such as “vibrant”, “robust”, “seamless” and “cutting-edge”, appear in clusters.
• Monotonous rhythm: sentences come out at almost the same length, with none of the breathing room that mixing long and short sentences provides.

Put the two lists side by side and a pattern emerges. The core of AI feel is not any single word but the regularity itself. Human writing naturally carries irregularity, redundancy and judgment calls about what to leave out. Models tend to produce high-probability, low-risk phrasing. De-slopping therefore comes down to putting that irregularity and information density back in.

3. Three Layers of De-slopping

Relying on a final rewrite alone will not give you consistent results. The work needs to happen at three layers at once.

Layer 1: Style constraints during generation

Setting style rules in the prompt is the cheapest and quickest step. The key is to give the model a specific list of what to avoid and what to include, rather than a vague instruction to “write naturally.”

Writing constraints (example):
- No formulaic transitions: 首先/其次/综上所述, delve into, it's important to note
- One core point per paragraph; do not force every point into a three-part parallel
- Vary sentence length; follow a long sentence with a short one
- Include at least one concrete detail: a number, a case, a date or a comparison
- Allow clear opinions and judgments; do not stay neutral at all times

The more specific the constraints, the easier it is to keep the model’s output on target. Spelling out what you don’t want often works better than describing what you do.

Layer 2: Rule-based post-processing

Once a draft is generated, run a set of automatable rules over it to find and replace the most common mechanical patterns. Keep the rules in a shared list that both people and scripts can work from.

# De-slopping rule scanner (illustrative)
CHINESE_PATTERNS = {
    r"综上所述|总而言之|不难发现|值得注意的是": "remove, or replace with a specific conclusion",
    r"不仅仅是.{0,20}更是": "rewrite as a direct statement",
    r"首先.*其次.*最后": "break into parallel points or a connected narrative",
}
ENGLISH_PATTERNS = {
    r"\bdelve into\b": "explore / look at",
    r"\bit'?s important to note that\b": "remove the lead-in sentence",
    r"\bin today'?s (fast-paced )?landscape\b": "replace with a specific time or setting",
    r"\ba testament to\b": "show / prove",
}
def scan(text, patterns):
    issues = []
    for pat, suggestion in patterns.items():
        for m in re.finditer(pat, text, flags=re.IGNORECASE):
            issues.append((m.group(), suggestion))
    return issues

Post-processing works like a health check, not an automatic surgery. The rules surface the problems. Whether to change each one, and how, should be left to someone with editorial judgment or to a more careful second round of generation.

Layer 3: Human review in the loop

Judging context is the hardest part for a machine to replace. Which parallel structures are worth keeping? When is a neutral tone actually the right choice? Where would a personal anecdote or an industry detail add something? Those calls have to come from people. The most effective workflow runs like this:

1. Generate a draft.
2. Scan it with the rules to locate problems.
3. Have a person rewrite the key paragraphs by hand.
4. Read through the whole piece once more for rhythm.

Combining the three layers keeps the process fast without sacrificing quality.

4. Turn the Rules into a Reusable Capability

A one-off cleanup has limited value. To get real gains in efficiency, turn the method into a rule set that the whole team can reuse and share. That usually means one of two things:

• a shared writing style guide
• a configuration file that AI coding tools or content workflows can load

Its value comes down to three points:

• Consistency: content produced by different people, or in different batches, follows one style, and its weaknesses stay under control.
• Iteration: as you use the rules, you keep adding newly spotted AI writing markers, so the list gets sharper over time.
• Bilingual reuse: the Chinese and English rule tables sit side by side. You build them once, and both languages benefit.

If you are building a content generation pipeline on Baidu AI Cloud’s large model services, keep the de-slopping rules in a separate module within the generate, check and polish flow, decoupled from the model itself. Your writing quality standards will then hold steady as the underlying model is upgraded, instead of drifting with each new model version.

5. Practical Tips

• De-slopping is not the same as stripping out information. Once the filler is gone, put concrete facts, data or examples in its place. Otherwise the article just gets emptier.
• Watch out for over-editing. Rule-based replacements can introduce a new kind of mechanical feel, so a human read-through is not optional.
• Set different thresholds for different scenarios. Marketing copy can carry some flourish, while technical documentation should stay more restrained. Adjust how strict the rules are to match.
• Keep feeding examples back into the system. Collect the drafts that get flagged as “very AI” and use them to improve the rule list, creating a positive feedback loop.

Conclusion

“AI feel” comes down to a model’s preference for high-probability phrasing. De-slopping is about putting back the irregularity, information density and clear stance that mark human writing. Break the work into three layers: generation constraints, rule-based post-processing and human review. Then turn the results into a reusable bilingual rule set. AI-assisted writing will then move from looking right to reading true. The approach does not depend on any particular tool. You can treat it as general engineering practice and build it into your content production or LLM application pipeline.