AI automatically generates tags and keyword descriptions at the same time, and manual review needs to check which items

After AI writing enters the mass production of articles, the accident patterns are completely different from those of manual writing: machines will not make a mistake in just one article, but will make a batch of mistakes according to the same error pattern.Therefore, the premise of review is to break the process into two stages - the generation layer is responsible for producing the copy, and the closure layer is responsible for blocking unqualified content before going online. AnQiCMS's AI capabilities are perfectly distributed across these two layers: automatically generating tags (AI auto-labeling) and assigning images and generating title keywords (TDK) when creating articles belong to the generation layer;Sensitive word filtering and keyword replacement, content review belongs to the mouth layer. The position of manual review is between the two layers to the mouth completion of this section, what to look at is the following four items.

What is responsible for the generation layer

Efficiency of the generation layer: From the theme to the finished text, AI automatically generates the main text, automatically tags to provide classification clues, AI draws pictures according to the theme, and TDK generates the title, keywords, and description all at once - these steps save a significant amount of time in batch scenarios. However, it is important to recognize the nature of the generated content: tags are the model's understanding of the text, descriptions are the model's summary of the text, and illustrations are the model's association with the title, all of which are 'model perspectives', not site perspectives. The review object is exactly this perspective difference.

What does the closing layer handle?

Risk solution at the closing layer, its characteristics are that it must be executed mechanically and not dependent on mood. In the content security settings of AnQiCMS, sensitive word filtering and keyword replacement, and content review constitute this process: expressions that match the word list are blocked or replaced with compliant writing, and manuscripts are decided whether they can enter the publishing queue according to the review status. The value of the closing is only magnified in batch scenarios - it is unrealistic to read each of the three hundred manuscripts one by one, but the premise that 'not to read each one by one' is established is that there is a machine closing layer that covers the bottom line, and manual processing only deals with the parts it marks.

The four most frequently problematic items in the review

The first tag drift: The automatic tag caught the literal meaning but not the intention. The manuscript writes 'server migration', and the tag gives a general word like 'server', causing the in-site classification to be biased.The review method is very light - look at the label after it is placed in the category page and whether it is like the same topic as other articles on the same page. The second item description does not match the text: The description in the TDK is the compression of the model for the text, and it is common to find 'promises conclusions not written in the text' in bulk, or key numbers in the description that cannot be found in the text;The description should be able to be quoted independently of the text, but the quoted content must truly exist in the text. The third item's illustration does not match the theme: the automatic title illustration works based on literal association, and abstract titles are most likely to produce illustrations that 'do not show any relationship'. When checking, only one question is looked at - can this illustration be recognized as the theme when placed back in the context of the text? The fourth item is the most expensive: It has already been queued after hitting sensitive words, usually because the closing rules are only attached to the manual submission process, while AI bulk submission takes a detour, and the configuration must ensure that the bulk link and the manual link pass through the same filter.

Where to place the closing step

Once the order is wrong, all the work before is in vain: the closing must be before the release, not after. If a problem is found after the release, the cost of handling includes the search snapshots already generated, the distributed references and the chain of recalls; if stopped before the release, the cost is only one rejection. The implementation is to fully utilize the state machine: AI draft enters the draft, passes the closing check (sensitive word filtering and keyword replacement, content review) and then enters the pending release, turning into the formal document at the designated time - if any step fails, the manuscript is returned to the draft instead of being sent out with the disease. 'Fixing after releasing' is a style issue in single manual writing, and an accident preset in batch AI production.

How to carry out random inspection for batch manuscripts

Full inspection is not feasible, random inspection should be done by category and layer: a fixed batch of articles should be randomly selected from each category to cover the shortest and longest manuscripts, manuscripts with numbers, and manuscripts that have hit the closing mark; if any of the three signals occur, the proportion of random inspection in that category should be expanded - the same label is repeated in the category (drifting signal), the descriptive sentences are highly similar (template generation out of control), and the pictures are similar (association collapse). The conclusion of the random inspection should be fed back to the word list and generation prompt layer, rather than manually changed article by article, and the solution to batch problems should be at the batch level.

Common questions

Question: Does automatic labeling still need manual correction?Answer: In the early stage, it is necessary to modify the 'word table' of the model instead of a single article: condense the mapping of high-frequency drifts into replacement rules, and the quality of tags will accumulate and converge with the rules, while manual modification of each article will never converge completely.

Question: Which of the TDK three items is most worth human inspection?Answer: description. It will be quoted directly by the search party and cited separately from the text. The harm caused by mistakes is most obvious; the title is wrong, it affects clicks; keywords are wrong, it affects classification. None of these have a wider impact than the former.

Question: With sensitive word filtering, is content review redundant?Answer: The two intercepts are different: the filter intercepts literal hits, and the review intercepts semantics and station rules (whether off-topic, whether overcommitment). Machine filtering is necessary but not sufficient, and this manual inspection step cannot be omitted.