An editorial workflow for useful AI-assisted content

Define the answer before the assignment

For this guide, our proposed starting point is a single reader question: “What should I prepare before requesting a service estimate?” Treat it as a fictional editorial exercise, not a description of an actual business. Before drafting anything, write down the answer the article should provide and the information you are authorized to use.

Our proposed assignment sheet has five fields: reader, question, scope, evidence, and reviewer. For the example, the scope might cover a project description, desired deliverables, available materials, and questions for the provider. Leave pricing and turnaround promises outside the assignment unless you have approved information to include. This is an editorial convention, not a claim that this format produces a particular commercial result.

Google’s content self-assessment asks whether the main heading or page title provides a descriptive, helpful summary of the content. Google’s people-first content guidance.

Use that question as a prompt for your own assignment review. Our recommendation is to write a one-sentence acceptance criterion: “The reader can prepare the information listed in this article without needing an unexplained term.” If that criterion does not suit your topic, replace it. Keep the criterion visible while choosing headings, examples, and supporting material.

Give the agent a bounded drafting task

Google describes generative AI as potentially useful for researching a topic and adding structure to original content. It also warns that generating many pages without adding value for users may violate its scaled content abuse policy. Google’s guidance on generative AI content.

Our proposed workflow assigns the agent a specific operation rather than an open-ended publishing instruction. Ask for an outline, a list of unanswered questions, or a draft section based on supplied material. Decide which operation you need before writing the prompt. Include the reader question, approved scope, and source material in that prompt.

For our fictional estimate article, a proposed instruction would be: “Create an outline explaining what information a reader should gather. Use only the supplied service documentation for statements about the provider. Mark missing information as a question for the editor.” Review the outline before requesting prose. Accept, replace, or remove each section yourself.

We also propose keeping a short record of the drafting task. Record the material supplied, the output requested, and the changes accepted by the editor. Do not treat this record as evidence for claims about the service. Keep the documentation supporting those claims in a separate evidence list, linked to the relevant statements.

Review the evidence sentence by sentence

Google explains that generative models predict likely word sequences and that their output can contain inaccuracies. Its guidance calls for manual fact-checking and review of AI-generated content before publication. Google’s explanation and review guidance.

Our proposed evidence review starts with highlighting externally checkable statements. Look for quantities, dates, product behavior, service conditions, and claims about what an action accomplishes. For every highlighted sentence, record the exact supporting passage. Then ask whether that passage supports the whole sentence or only part of it.

Consider this invented drafting example: “The request form accepts attachments and guarantees a response within one day.” In our exercise, documentation showing an attachment field would support only the first part. Unless another approved passage establishes the response promise, remove that promise or leave a question for the responsible reviewer. This example makes no assertion about a real request form.

Use four editorial decisions: retain, narrow, investigate, or remove. Our recommendation is to resolve each decision before sign-off. If you wish to add a suggested practice, introduce it as your proposed workflow or checklist. Do not attach a citation to a recommendation merely because its general subject appears in the source.

Google extends its review guidance to title elements, meta descriptions, structured data, and image alternative text. Google’s scope for content review. In our proposed checklist, review these items alongside the article. Compare the title’s promise with the actual answer, and check whether the description introduces any assertion absent from the approved copy.

Choose the format that fits your question

Our editorial convention offers three possible formats: an explanation, a procedure, or a comparison. Choose one according to the assignment. For “What does this mean?”, propose a definition with an example. For “How do I prepare?”, propose an ordered checklist. For “Which option fits my needs?”, propose parallel criteria and explicitly supported descriptions.

For the fictional service-estimate article, we would choose a procedure. Draft a section for each piece of information the reader should prepare. Under each heading, add a short explanation of what to write and an example clearly marked as invented. Where a requirement comes from the provider’s documentation, cite that documentation rather than treating the example as proof.

Google explicitly says it does not have a preferred word count. Google’s content self-assessment questions. Our proposed editing rule is to retain material that answers the assigned question and remove material that repeats an answer without adding necessary detail. If your publisher specifies a length, treat that as a separate editorial constraint.

At this stage, read the headings without the paragraphs. In our checklist, each heading should identify the subject of its section. Then review every example: is it labeled, does it match the intended task, and does it introduce unsupported details about a product or service? Rewrite any example that fails your acceptance criterion.

Commission language versions separately

Google distinguishes a multilingual site, which offers content in more than one language, from a multi-regional site, which targets users in different countries. Google’s multilingual and multi-regional documentation.

Our proposed localization workflow treats each language version as its own assignment. Share the verified evidence list, but ask the local editor to restate the reader question, choose appropriate terminology, and develop examples independently. Do not require identical paragraph order. For our exercise, an English editor might organize the article around deliverables, while a Polish editor might organize it around the materials needed to describe the request.

Google recommends separate URLs for language versions and hreflang annotations to help its search results link to the appropriate version. Google’s recommendations for language URLs. Our proposed handoff includes an explicit list of the intended language URLs and their relationships for the person implementing publication.

Google uses visible page content to determine language and recommends one language for content and navigation on each page. It advises against automatically redirecting users between language versions and suggests links through which users can choose another version. Google’s page-language and switching guidance.

For our localization review, inspect the article, button labels, captions, and page description as separate pieces of writing. Choose a consistent term for each repeated action. Ask the local reviewer to rewrite passages they would not naturally use, while preserving every verified factual boundary from the evidence list.

Make publication a deliberate handoff

Our proposed publication workflow has three review stages. First, confirm that the article answers the assignment. Second, close all evidence questions. Third, inspect the assembled page, including links, headings, captions, metadata, and the call to action. Assign a named person to make the publication decision and record any unresolved issue instead of silently passing it onward.

The Max AI registration page presents Max AI as an operating system for AI agents. Max AI registration page. To begin with an account, use Create account. For the workflow proposed here, prepare your assignment sheet, approved source material, and review criteria before giving an agent a writing task.

After publication, our recommendation is to maintain a revision queue with a reason for each proposed change. Examples might include a revised service document, a reader question requiring investigation, or an editorial decision to expand the scope. Record the relevant passage, the proposed amendment, and the reviewer. When revising, repeat the evidence check rather than treating previously approved wording as permanent authority.