A small editorial team with AI: from sources to publication

There is a new model. What do we want to write about?

A manufacturer’s announcement may give us a long feature list. We also need a reader’s question. For example: “Will this model help me animate something for my website?” Now we know what to look for and which example to show. A feature list alone will not organize the article.

We suggest splitting AI-assisted work into three roles: sourcing, editing and reviewing. You can assign a team or move through the stages yourself. Findings first, then text, then comments on specific sentences. That does not guarantee an error-free article. It does make it easier to spot when a loose idea starts masquerading as verified information.

The researcher brings a list of unknowns too

The researcher — the person gathering and checking materials — prepares questions, sources and findings. For product information, they record a direct link, date and who has access. An unperformed test, unclear limit or name found only in an unofficial mention goes separately into the unknowns. The editor needs to know what is actually available.

The title can wait. Falling in love with an attractive premise too early makes it harder to notice the sources do not support it. Choose the format after reviewing the material: a full guide, short news item or perhaps just a link in a roundup. Not every announcement needs a whole article.

The editor shows why anyone would want the feature

Back to animation. A reader might benefit from choosing a scene, building its movement and adjusting the timing. That example makes the tool’s use easier to understand than another feature list. Show where a person still has to make a decision too.

Keep the distinction between an idea and completed work. If we write “we tested,” there must be a trial behind it. An invented exercise can still be useful; simply call it a suggestion. Google’s guidance on AI-generated content emphasizes reader value and manually checking accuracy. A larger number of articles alone does not deliver either.

The prompt below helps build a draft from a prepared source package. The final checklist is for the editorial team. Keep it through later revisions so the questions do not disappear with the working draft.

Edit an article using the attached source package.
Start with a specific reader problem.
Separate confirmed features, a suggested use and unknowns.
Do not invent tests or the author’s experiences.
Use ordinary language; explain terms on first use.
Finish with a list of claims that need further checking before publication.

“Sounds good” is not yet a review

The reviewer revisits names, dates and links. They look for sentences where an announcement became a release or access for selected accounts became availability for everyone. If there is code, they compare its behavior with the caption. The author needs a comment on a specific passage, not just a general verdict.

After corrections, read the title, listing excerpt and article together. A short preview can overpromise too. Leave a note identifying who checked the main findings and which questions remain open.

Also agree who will return to the article after the product changes and how an update will be marked. A note alongside the topic is enough in a small team. When availability changes a few weeks later, you will know whom to tell and where to correct it.