ChatGPT prompt examples for SEO: 25 use cases

The most common problem teams face when using AI tools for SEO work is output that stays generic and shallow. The cause is usually not the tool itself but a vague instruction. In this article we share ChatGPT prompt examples for SEO: 25 use cases split across five areas of work, with notes on how to verify the output in each. Treat the examples as templates; you will get more precise results when you fill the fields in square brackets with your own data. We covered the limits of producing articles with AI from the perspective of Google's policies separately in AI article writer.

How to build a good ChatGPT prompt

OpenAI's prompt engineering guide recommends splitting instructions into clear sections, giving a role and context, and sharing example inputs and outputs when needed. For SEO, this translates into four parts: the task (what is being asked), the context (site, audience, language, market), the input (the data you provide) and the output format (table, list, character limit). Expecting the tool to produce current search volumes, rankings or traffic data on its own is a mistake; numbers like these need to come from Search Console or your keyword tool and be given to the prompt as input. All the examples below were written on that principle.

Research and keyword use cases

• "Classify the keyword list below by search intent as informational, comparison, transactional or brand; return it as a table: [list]"
• "In this Search Console query export, cluster the queries that search for the same topic in different words and suggest a single main page for each cluster: [data]"
• "List the questions a user might ask about [topic] during their decision process, split into awareness, consideration and decision stages."
• "Compare the title and H2 lists of the two pages below; show the sections that target the same intent and risk competing with each other: [data]"
• "Suggest synonyms and local expressions users use for [industry]; mark which ones need to be verified in a keyword tool."

Content planning and writing use cases

• "Prepare a content outline that fits the search intent for [keyword]: H1, H2 headings and the question each heading will answer."
• "Read this outline and list the areas of experience and examples an expert would add but that are missing from it: [outline]"
• "Find the claims in the text below that need evidence and write what type of source each one requires: [text]"
• "Rewrite this paragraph in shorter, clearer sentences without changing its meaning; keep the technical terms: [paragraph]"
• "Write three meta description suggestions for this article, between 115 and 160 characters, containing [keyword], and state their character counts."

Technical SEO check use cases

• "Read the robots.txt file below; explain which directories are blocked for which crawlers and any blocks that may be unintended: [file]"
• "Check the canonical, hreflang and meta robots tags in the head section of this page; list any inconsistencies: [code]"
• "Find the rows in the redirect list below that create chains or loops: [list]"
• "Create a schema.org structured data draft for this product page; do not add information that is not visible on the page: [page content]"
• "Prioritize this crawl report summary: show the items with high impact and easy implementation at the top: [report]"

Localization and multilingual use cases

• "Adapt this Turkish title to the phrases users would naturally search for in the English, Arabic and Persian markets; do not translate word for word: [title]"
• "Compare the terms in this translation with the glossary below and show inconsistent usage: [translation] [glossary]"
• "List the mixed-direction expressions in the Arabic version of this text that may cause problems in a right-to-left layout: [text]"
• "Match each language version for the URL list below and show pages that have no counterpart: [list]"
• "Flag the examples on this page that do not suit the local market, and details such as currency and date format: [page]"

Reporting and AI visibility use cases

• "Compare this monthly Search Console data with the previous month; summarize the pages that changed most and the possible reasons, based only on information in the data: [data]"
• "Turn this report summary into a five-point decision note that a non-technical manager can understand: [summary]"
• "Create a consistent logging table template for testing these 20 questions about our brand in AI assistants: [questions]"
• "Identify the short, self-contained passages on this page that could be quoted in an AI answer; suggest what is missing: [page]"
• "Summarize in a table the questions in the test results below where our brand was cited as a source and where it was not: [results]"

Manage the prompt library as a team

The use cases above save time when used one by one; turned into a shared library within the team, they provide consistency. The setup that works in practice is to keep a short record for each prompt: what task it is used for, what inputs it expects, what format the output comes in, and who last updated it and when. When a prompt gives an unexpected result, this record makes it easier to tell whether the problem came from the instruction or from the input.

The library's second rule is that confidential or personal data is never entered into prompts. Anonymized examples should be used instead of client names, patient information or contract details. The third rule is to retest the most frequently used prompts whenever the model or tool changes; the same instruction can produce results at different levels of detail on different models. This small discipline keeps the use cases working at the same quality months later. Keeping the library in a single document everyone can access, and archiving old versions instead of deleting them, also makes it possible to track the changes made.

Rules for verifying the output

However well these ChatGPT prompt examples are built, the output should not go live without verification. We recommend four rules. First, numbers should come only from the data you provided; statistics the model produces on its own should not be used. Second, technical recommendations should be tested on the live page. Third, in areas such as health, finance and law, the text should be reviewed by an expert. Fourth, you should ask whether the published content really helps the user. Google explains that it looks at whether content is helpful rather than how it was produced; its helpful content guidance highlights the questions of who created the content, how and why.

We describe how we work on the content side on our content SEO service page, and how we measure visibility in AI assistants on our AI search visibility page. If you want the detailed setup for prompt-based measurement, see our 100-prompt benchmark article. None of these examples is a guarantee of rankings or of appearing in an AI answer; their purpose is to help your team do the same work more consistently and in a way that can be audited.

Sources

• Prompt engineering — OpenAI API
• Google Search's guidance about AI-generated content — Google Search Central Blog
• Creating helpful, reliable, people-first content — Google Search Central