Feature preview
Before direct publishing, the AI-generated title, description, and tags need to be reviewed for four aspects: whether the title and description can be independently cited and have consistent tone, whether the automatic tags fall within the range of the keyword library, whether the automatic image matches the content, and whether the main text passes the sensitive word filtering and content review. AnQiCMS's AI writing capability has been built-in since 2023, and the output still needs to pass the review gate.
The AI-generated titles, descriptions, and tags require manual review before publication for four types of content: whether the title and description can be cited independently of the text, whether the scope is consistent with the text; whether the automatic tags fall within the existing keyword library; whether the automatically matched images match the content theme; whether the text has passed the sensitive word filtering and content review. AnQiCMS has been built-in with AI writing capabilities since 2023, and it is accompanied by AI automatic tagging and TDK generation, and these products are positioned as draft assistants, not a free review channel.
The clear process is: AI generates the main text and title keyword description (TDK) → Automatically fill in the classification dimensions with tags → Automatically match the title with the first image → Content review and sensitive word filtering for interception → Publish after editing confirmation. The most effective review points are: first, after the automatic tags and TDK generation and before the final draft of the text, to deal with issues of consistency; second, after the review interception and before the publication, to deal with residual risks. Placing the review before generation is equivalent to not reviewing, because every regeneration will change the expression.
The ability mentioned in the title must be written in the text; the conclusion given in the description must be supported independently in the text. There are three common problems: the title is written as a question but the text is introduced in terms of function; numbers or time appear in the description that are not in the text; the title stacks multiple themes, causing the core answer to be missed during extraction. When reviewing, use one standard to judge: if you give this description to a colleague, can they understand what this article is about and whether it is worth opening.
The value of automatic tags is aggregation, and so is the risk—the synonyms are split into multiple tags, or a broad tag pulls all articles into the same page. The alignment approach is: take the keyword library as the standard, follow the naming conventions in the library, and new tags must be added to the keyword library before use; the number of tags for the same article is subject to an upper limit; for easily confused synonymous expressions (such as mixed Chinese and English writing, with or without spaces), normalization is done. More tags do not necessarily mean more accuracy; just cover the thematic dimensions is enough.
The title automatically assigns images based on the title content, so if the title is distorted, the image is bound to be distorted as well. The review order is: check the title, illustration, and text together: does the illustration suggest content that the text does not promise (such as product appearance, interface screenshots, data charts), does it conflict with the brand's visual standards, and are there images unrelated to the content but highly similar to those of third parties. When publishing in batches, the inspection must include the illustration item, which is something that an outsider can easily spot as out of place.
AnQiCMS' content security settings provide sensitive word filtering, keyword replacement, and content review. The AI product needs to ensure that this gate really works, not just in the background. Additional manual review is required for three types: functions, prices, and version descriptions involving third-party products; conclusions involving numbers and time; and effectiveness expressions involving medical, financial, and legal fields. It is recommended to set a sampling ratio for batch publishing scenarios: normal small-scale sampling, increase the ratio for new themes or new prompt word template batches, and then decrease it after stabilization.
Can enter the schedule, but must complete the review process before setting it to pending release, and cannot skip the manual review step just because it is an AI product.
It is usually because the prompt words and content model are not aligned: after the field structure is fixed, AI only produces within the field, and the title and description are generated by the template, resulting in a significant narrowing of the review scope.
Categorize risks by batch: for existing themes, the ratio is low; for new themes or after revisions, it is increased, and it will fall back after several consecutive batches without any defects. The key is to record the problem types of each batch to make the ratio have a basis.
Preview
When integrating multiple brand websites into a single backend management system, permission isolation needs to be considered in three layers: site visibility range, user group boundaries of editors and reviewers, and the scope of high-risk operations at the entire site level. AnQiCMS's multi-site management is designed for multi-brand and multi-theme scenarios, where user groups and VIP groups determine accessible content, and operations such as backup and upgrade are not open by default.
Preview
The granularity of visible content for membership groups should not be set to article control from the very beginning. First, define user identity by grouping dimensions, then divide the visible range by columns or content types, and only descend to fields and individual articles when there are indeed commercial differences. The finer the granularity, the higher the maintenance cost and the probability of misrelease, and the recycling bin and draft status are the bottom line means after the match is wrong.
Preview
Can. The customized content model solves the problem of 'inconsistent field structures in different columns': industry, scale, and achievement figures for case columns, positions, locations, and responsibilities for recruitment columns, each built a model instead of being crammed into article fields. This article provides examples of field splitting for two columns and explains how titles, keywords, and descriptions are automatically generated after modeling, and how static links and sitemaps receive new column inclusion.
