To match search intent with AI, work out what the searcher is trying to do before you prompt: look at the live results for the query, decide whether it is mainly a "know", "do", website or visit-in-person query, note which interpretation most people mean, and then tell the model that intent explicitly along with the format it implies. AI is useful for classifying queries, listing sub-questions and checking a draft against the intended task, but it cannot see today's results unless you give them to it or use a search-grounded tool. The final test is behavioural: whether real searchers click and stay, which Search Console shows you after publication.

This guide uses the intent framework in Google's own Search Quality Rater Guidelines, the common four-type model used by SEO tools, and vendor documentation on prompting.

What search intent means

Google's Search Quality Rater Guidelines (version dated 11 September 2025, checked October 2026) give the clearest public description of how Google thinks about intent. Section 12.7 suggests thinking of queries as having one or more of four intents:

  • Know: the user wants information or to explore a topic. A subset, Know Simple, seeks a specific answer that most people would agree on and that fits in one or two sentences or a short list.
  • Do: the user wants to accomplish a goal or activity, such as downloading, buying, obtaining or interacting with a site or app.
  • Website: the user wants a specific website or page.
  • Visit-in-person: the user wants a physical place, either a specific business or a category of businesses nearby.

The guidelines add that the line between Know and Do "is sometimes blurry" and that many queries fit more than one category. Google is explicit that raters do not directly influence rankings; its guidance on generative AI content says the guidelines "are not a guide to ranking first in Google". They are still the most detailed description Google publishes of what a good result looks like for a given query.

SEO tools commonly use a related four-way split. Semrush's guide to search intent describes informational, navigational, commercial and transactional intent, with commercial covering people researching before a purchase. Moz's search intent explainer uses similar categories. Either model works for briefing an AI tool, as long as you apply it consistently.

Interpretation comes before intent

Many queries mean different things to different people. The rater guidelines (section 12.5) distinguish a dominant interpretation (what most users mean), common interpretations (what many or some users mean) and minor interpretations, which may be reasonable or unlikely. Their example is [apple], which may mean the company or the fruit.

Interpretations also move over time. The guidelines show how [iphone] meant the first model when it launched in 2007 and now usually means the latest or upcoming model. Locale matters too: section 12.3 asks raters to consider whether people in one city or country would look for something different from people elsewhere. A searcher in London and one in Chicago typing "football" are likely thinking of different sports.

For writers, this means a model's first guess about a query reflects its training data, not today's results. Google's documentation reminds publishers that generative models "predict a likely sequence of words based on their training data". Check the interpretation yourself.

Read the results before you prompt

The fastest evidence of intent is the results page itself. Search the query in the locale you are targeting and note:

  • What types of page rank: guides, product pages, tools, videos, local listings, forums.
  • What format dominates: step-by-step instructions, comparison tables, definitions, lists of options.
  • What features appear: a featured snippet, a "People also ask" box, a map pack, an AI Overview.
  • What the top pages have in common, and what they miss.

Google's page on AI features and your website explains that AI Overviews and AI Mode may use "query fan-out", issuing multiple related searches across subtopics to build a response, and that AI Overviews "often don't trigger" because they are shown only when Google's systems judge them additive. The related searches on a results page are a useful hint at those subtopics.

Most chat models cannot see live results by default. Google's Gemini prompting strategies note that grounding with Google Search connects the model to real-time web content and should be enabled when the model may need recent facts. Without grounding, paste the titles and snippets of the top results into your prompt yourself.

Prompts for intent analysis

Once you have the evidence, AI is good at organising it. Anthropic's prompting best practices recommend clear, specific instructions and wrapping different inputs in their own tags so the model does not confuse data with instructions. Three prompts cover most needs.

Classify the query. "Here are the titles and snippets of the top ten UK results for [query], inside <results> tags. Classify the dominant intent as Know, Know Simple, Do, Website or Visit-in-person, list any common secondary interpretations, and explain your reasoning from the results only."

List the sub-questions. "Based on these results and the People also ask questions in <paa> tags, list the questions a searcher with the dominant intent is likely to have, in the order they would ask them. Mark any that the current top results answer poorly."

Check a draft. "Here is the intent analysis and my draft. For each section, state which sub-question it answers. List sub-questions the draft does not answer and sections that serve a different intent."

The last prompt is the most valuable. It turns intent from a planning label into a test the draft must pass.

Writing for each intent

Know Simple. Put the answer in the first sentence or two. The rater guidelines say a Know Simple answer must be "correct and complete" and fit in a small space. Supporting detail can follow, but the answer cannot be buried.

Know (broader). Readers want to explore. The guidelines' example of [retirement planning] notes that different users "would be better served by different information". Structure the page so readers can find their own situation quickly, with headings that name each case.

Do. Readers want to complete a task. Lead with the steps or the tool, state prerequisites up front, and remove anything between the reader and the action. Google's SEO Starter Guide also reminds publishers that people use different words for the same need, citing "charcuterie" and "cheese board", so cover the phrasings your readers use.

Commercial research. Readers compare options before deciding. Give consistent criteria, explain trade-offs and say how you assessed each option. Google's helpful content guidance notes that, for reviews, readers trust content more when they understand how many products were tested, what the results were and how the tests were run.

Website and visit-in-person. These are usually best served by the destination itself: a brand's own site or a local listing. A blog article rarely wins these queries and seldom should try.

Common mistakes with AI and intent

  • Letting the model guess the intent. Without results in the prompt, it answers from patterns in its training data, which may be out of date or from another country.
  • Writing for a minor interpretation. An article on an unlikely meaning of a query will not satisfy most searchers, however good it is.
  • Mixing intents on one page. A how-to that turns into a product comparison halfway through serves neither reader well. Split it into two pages and link them.
  • Mistaking format for intent. Copying the structure of the top result is not matching intent. Google's helpful content questions ask whether a page provides "substantial value when compared to other pages in search results".
  • Scaling without checking. Google's spam policies list "using generative AI tools or other similar tools to generate many pages without adding value for users" as an example of scaled content abuse. Producing a page for every keyword variant, each with the same generic answer, is exactly that.

Measuring whether the match worked

After publication, the Search Console performance report shows the queries that bring people to each page, with clicks, impressions, click-through rate (clicks divided by impressions) and average position. Filter by page and look at the query list.

Signals worth acting on:

  • Queries you did not target with many impressions. These reveal the interpretations Google associates with your page. Either serve them better or adjust the page so it stops attracting the wrong ones.
  • High impressions, low click-through rate. The title or snippet may not match what searchers expect. Rewrite the title to describe the content more precisely.
  • Clicks but short visits (in your analytics tool). The page may answer a different question from the one searchers have.

Search Console's documentation notes that results are specific to the time, place, device and recent history of the person searching, so do not judge a page by a single manual search. For seasonal topics, Google Trends can show whether interest in a query changes through the year, which may shift its dominant interpretation.

Revisit intent whenever results change substantially. The rater guidelines' iPhone example is a reminder that what people mean by a query is not fixed. Our sister site AI Disraeli UK covers related writing workflows for British audiences.