How to use AI tools to assist writing without being flagged as AIGC junk?
Chapter 4: Content Marketing · Question 54
Background: The "double-edged sword" dilemma of AI writing
Since 2024, Baidu and Google have both stepped up their crackdown on low-quality AIGC content. Baidu has explicitly stated it will demote "AI-generated content lacking added value," AI-generated content", and Google's Helpful Content Update is also specifically filtering out mass-produced AI output.
But the problem is—AI really can greatly boost writing efficiency. Avoid it entirely and efficiency drops; abuse it and you get penalized by search engines.
The key is:how to use it, and at which stage.
The right way to use AI-assisted writing
Stages you can safely hand over to AI
| Stage | Description | Risk level |
|---|---|---|
| Topic research | Have AI analyze keywords and list common user questions | ✓ Zero risk |
| Outline generation | Give it a topic and have AI produce a structural framework | ✓ Zero risk |
| Draft expansion | Based on your key points, have AI expand the paragraphs | Note: requires rewriting |
| Data organization | Format tables, summarize comparative information | ✓ Low risk |
| Polishing and proofreading | Check grammar, optimize sentence fluency | ✓ Low risk |
Stages that must be completed by humans
- Inject real experience—What pitfalls have you hit? What have customers given feedback on? This is something AI cannot make up
- Add specific data——“We tested for 3 months, and the conversion rate rose from 2.1% to 3.8%" (the above is sample data, not an industry average)
- Form a unique viewpoint——Don't just give a two-sided discussion like "A has advantages and disadvantages"; take a stance
- Supplement with first-hand screenshots/cases——Real backend screenshots and customer conversation screenshots are the strongest "human touch" signals
The core logic by which search engines determine AIGC junk
Search engines are not detecting "whether this article was written by AI”; they are detecting:
- Whether there is information gain——After reading this, can users get something that the first 10 pages of search results don't have?
- Whether there are experience signals——Is there evidence of E-E-A-T(Experience, Expertise, Authoritativeness, Trustworthiness)?
- Whether it is mass-produced——Does the same site publish a large amount of structurally similar content in a short period of time?
So, an article assisted by AI but infused with real cases is higher in quality than a purely human-written article that is pieced together.
Practical workflow: my "70/30 rule"
My daily content production process is like this:
- Human sets the direction(5 minutes)——Determine the topic, targetKeyword, and core viewpoint to convey
- AI produces the framework(3 minutes)——Generate an outline + key points for each paragraph
- Human fills in the material(20 minutes)——Insert real data, cases, and screenshots
- AI polishes(5 minutes)——Optimize transition sentences and check logical coherence
- Human does final review(10 minutes)——Remove "AI-flavored" expressions and ensure it reads like a real person speaking
70% of the value of the entire article comes from human input (experience, data, viewpoints), and AI is responsible for 30% of the efficiency improvement (structure, polishing, formatting) (this is the author's "70/30 rule" empirical ratio, not precise statistics).
Use the "AI judgment framework" to score the draft
What was discussed above is "how to prevent being judged as AIGC garbage", conversely, how do you know whether what you've written is "good content"? You can self-check using the three dimensions of the "AI-era content evaluation framework" (see this series Q65: How do you judge content quality in the AI era?）：
| Evaluation dimension | Self-check question | Signal of failure |
|---|---|---|
| Factual reliability | Do my data/opinions have sources? | All adjectives, no numbers, no sources |
| Information increment | Could AI write this content out of thin air? | Restating common knowledge, no independent judgment |
| Parsability | Can AI easily extract my answer? | No subheadings, no lists, one big block of text |
These three dimensions and the above "logic by which search engines identify AIGC garbage" are actually two sides of the same coin:What search engines look at is "whether there is information gain"; what the AI evaluation framework looks at is "how much information gain there actually is." Only content that passes both sets of checks at the same time is truly in the safe zone.
Common "AI-flavored" expressions to watch out for
- “In today's digital era...”
- “In conclusion, to sum up...”
- “It is worth noting that...”
- “This not only... but also... more importantly..." (three-part progression)
- Overuse of "empower"“"assist"“all-around”
Practical recommendations
- Build a "material library" habit: routinely jot down customer feedback, test data, and industry observations, and call on them directly when writing. This is the biggest weapon for freeing content from the AI flavor
- Let AI assist with only one stage per article: don't throw everything to AI from start to finish. Even manually rewriting just two paragraphs will make the overall quality qualitatively different
- Do a "deletion test" before publishing”: mark all sentences in the article that could be written without real experience. If they exceed 60% (experience threshold), it means there isn't enough human flavor and you need to add more
