Collect questions from different sources and record where they came from
Useful sources include genuine support and sales enquiries, interview findings, site search, saved AI answers and available Google or Bing data. Agent suggestions are also useful for exploring a topic, but remain hypotheses. Record the origin and the person’s task for each item. For example, “how to connect” could mean installing something independently, choosing a compatible tool or finding a contractor. Without clarification, this is not one query with one answer. Do not call the number of formulations you found monthly ChatGPT demand: an idea, search engine impressions and query volume inside an AI chat are different kinds of data.
Group intentions, then choose one primary page
Phrases such as “which service to choose,” “solution comparison” and “what suits a small team” may concern a similar decision, but the context needs checking. Group them when the person wants the same outcome. Do not create a separate URL for every word rearrangement or similarly named AI model. For each group, record the primary page and the question variants it covers. This creates an architecture: a service page explains the offer, a guide explains how to complete a task, and a comparison explains selection criteria. The group heading should communicate a benefit to the reader rather than simply repeat a keyword phrase.
First map the plan to the existing website
Open the current pages and check how well they already address the intent. Sometimes adding missing conditions to a product page is enough. A separate article is needed when the task stands on its own and can be covered thoroughly. Useful plan fields include audience, intent, existing URL, missing information, intended result, responsible person and verification criteria. For the first cycle, choose a few important groups for which you have facts and can implement changes. Google’s guidance on helpful, people-first content supports focusing on the reader’s needs; the number of pages published does not itself establish quality or success in AI answers.
Use cited sources as a starting point for research
If a saved answer contains a competitor and a source but no link to your website, that is a useful signal to investigate. Read the specific material: what problem it solves, which facts it presents and what your website lacks. Do not assume that a URL appearing in an answer already proves the page’s contents or that you can publish there. A source gap in AURA is tied to a particular question and observation; the agent still needs to inspect the source. Plan a useful addition or a separate article when the business has something it can substantiate. Copying someone else’s structure without your own evidence does not close a real gap.
Separate the editorial plan from outcome measurement
In AURA, content_faq ideas are stored separately from monitoring. A FAQ question can suggest a guide topic, but its answer does not measure a natural recommendation of the service to a buyer. For monitoring, save neutral selection questions and repeat them in a comparable context through an external agent that is actually available. Set a completion criterion for the editorial task: the required material is published at the right address, connected to the relevant service and verified. Then review new answers, citations and visits separately. Marking “article written” completes the content production stage, but does not replace indexing or a real audience response.
Conditions and limitations
- Agent suggestions are not AI query volumes
- A source gap requires reading the original page
- Producing content does not prove indexing or citations
Explore the details
An intent map with primary URLs, a focused priority plan and completion criteria
Build a useful content plan