Feature preview
When AI completes the manuscript and simultaneous human review, the quality is controlled by three layers separately: the generation layer is responsible for output and automatic labeling, the rule layer uses sensitive word filtering and keyword replacement to intercept certain issues first, and the manual layer relies on draft preview and pending release status for control. The sequence is that the rule layer takes effect before the manual layer, and humans only deal with judgment issues left by the rule layer, and only mass output can sustain.
When AI-written manuscripts and manual review run simultaneously, the quality is not controlled by a single layer but is divided into three layers based on the certainty of the issues: the generation layer is responsible for producing content, automatic labeling and image matching, the rule layer uses sensitive word filtering and keyword replacement to intercept certain violations, and the manual layer only makes judgmental reviews between draft preview and pending publication. The rule layer must take effect before the manual layer, otherwise the number of issues faced by the manual review will be so large that it will be unreviewable.
The layer receives 'whether or not': Is the content segmented, have tags been generated, and are the keywords in the title description complete? AnQiCMS has been providing AI writing since April 2023, with AI automatic tagging, AI painting, and TDK generation, and these outputs are performed in parallel during a single posting action.
The rule layer receives 'Is it correct?' : prohibited words, exaggerated expressions, contact information, and incorrect brand writing. This layer's characteristic is that the criteria can be written into a word table, so it is suitable for the machine to run first. The sensitive word filter is responsible for intercepting, and the keyword replacement is responsible for correcting those words that are not standardized in writing but have clear intentions, such as changing the old event name to the current standard.
The manual layer receives 'Is it good or bad?' : whether the facts are established, whether the conclusion answers the question in the title, and whether the paragraphs contradict each other. These three things do not have a reliable word table to rely on, and can only be done by humans.
The cost of reversal is direct. Suppose a batch of manuscripts has two thousand pieces, with an average of three problems per manuscript in the rule layer, the manual layer has to judge in six million text locations, and most of them are the same batch of repeated errors. The rule layer runs first, and the manual layer gets the filtered inventory, and the workload changes from word-by-word modification to only looking at the paragraphs that need to be judged.
The configuration of sensitive word filtering and keyword replacement in the content security settings should be executed before the content is stored, not when it is rendered on the page. The replacement during rendering only changes the display, and the违规 statement is still there in the data, it will come out again when changing the template or exporting.
The rule layer has another easily overlooked responsibility: to keep the hit records. When the manual review sees 'This article has been blocked by the rule layer twice,' it knows which two paragraphs to focus on, rather than reading from the beginning.'
It is not realistic to manually review the entire batch output. Inspections should be allocated according to risk rather than evenly distributed. A feasible method is to divide into three levels:
New model, new selection, new prompt batch - fully reviewed in the first week to ensure stable tone; regularly ordered items that have run smoothly - reviewed at a ratio of one out of five, focusing on the conclusion section and numerical sections; manuscripts involving price, version, and policy statements - only these paragraphs are selected, but from each article, without skipping.
The issues extracted need to settle in the rule layer. When the same type of error occurs for the second time, it should be changed from manual judgment to a word table rule, allowing the rule layer to take over, which is the premise for the gradual decrease in the proportion of checks. Conversely, if several rounds of checks all find that the same type of issue has not been added to the word table, it indicates that the rule layer is standing still, and the manual layer will be repeatedly occupied by the same error.
The review should have a specific point, otherwise 'seen' and 'approved' cannot be separated. The document status is divided into formal documents, drafts, pending release, and recycling bin, the content written by AI is first put into the draft, and the draft link with the preview parameter can be previewed, the reviewer does not need to make it public, nor worry about mistakenly publishing it.
The pending release status is responsible for the segment 'has been approved, only waiting for time'. It is配合 with scheduled release, it can decouple the conclusion of the artificial layer from the online time: the review is completed during the day, and the release is scheduled during the traffic peak period, without the need for anyone to be on duty.
The last step is to catch everything in the recycle bin. Instead of physically deleting drafts that are not approved, they should be moved to the recycle bin so that they can be retrieved for reference when needed, which is especially useful for drafts that are screened out in bulk.
Can we rely solely on the rule layer without manual review?Issues that can be covered by the vocabulary list can indeed save manual time, but factual errors and questions that are not answered are two types that cannot be written in the vocabulary list. Skipping the manual layer results in problems going live directly.
What should be done if errors are found after the review is passed?First, transfer the draft for modification, and then go through the pending release process again. Directly modifying published content can easily result in missing cache and push notifications.
Should the proportion of spot checks be fixed?It is not recommended. Allocate by category risk, and in each round, add the newly discovered error types to the rule layer, and the ratio decreases as the coverage of the rule layer increases.
Preview
The rows that need to be filtered, reused, and batch imported in the product parameter table should be made into custom fields, and descriptive paragraphs should be left in the rich text of the main text. Fields are responsible for structured data, and templates are responsible for rendering and default handling. When batch importing, the column names of the table header and fields correspond one to one. Whether the parameter table can be understood by the machine depends on whether the division of labor is clear in this case.
Preview
AI mass production of articles needs to be divided into two processes: generation layer and closure layer: automatic tagging, AI painting matching and TDK keyword description belong to the generation layer, improving efficiency; sensitive word filtering and keyword replacement, content review belong to the closure layer, determining risk. Manual review focuses on the four items most likely to cause problems - tag drift, description inconsistent with the text, matching picture not consistent with the theme, and hitting sensitive words but still entering the queue - and fixes the closure before publication, batch scenarios are inspected by category.
Preview
AI painting, automatic tagging, and AI TDK save the three stages of manual work in the content production process: picture matching, classification, title, and description writing. Content sites with a large volume of postings and a fixed number of human resources have the most direct benefits, while personal blogs and low-frequency sites have limited benefits. Sensitive word filtering and content review at the intake level cannot be omitted, and AnQiCMS configures the generation and review in the same process flow.
