Researching AI Prompts — How People Ask AI About Your Topic

User Questions in the AI Age: Why Classic W-Questions Are Now Prompts

The way people search for information is changing at a fundamental level. While users used to enter short, keyword-like terms into search engines, they are now increasingly formulating complex, conversational queries. This development affects classic search engines as well as dialogue-based AI systems, including ChatGPT, Perplexity, and Google AI Overviews.

Simple W-questions have evolved into detailed prompts. These inputs come with a specific context, a clear expectation regarding the format, and often a defined target audience. For search engine optimization, this means that merely covering individual keywords is no longer sufficient to appear in the results.

The focus is now on capturing the actual user intent at a deeper, semantic level. When users query an AI, they generally don't expect an unannotated list of links. They are looking for a synthesized, direct answer that solves their problem. Industry forecasts suggest that the volume of traditional search queries will decline in favor of chatbot interactions in the coming years.

Content should therefore be prepared in such a way that it can be more easily used by these systems as a reliable source of information. The origin of the classic W-questions – Who, What, When, Where, Why, How – lies in journalism, where they ensure the completeness of a report. In modern content marketing, they continue to serve as a foundation for forming thematic clusters.

However, merely answering these questions is no longer enough. The structure of the answer largely determines whether a text is machine-readable and quotable for Large Language Models (LLMs). An effective strategy aims to create content that is precisely tailored to the information needs of the target audience.

If a website provides answers that directly address frequently asked prompts, the likelihood increases that AI systems will extract these passages and reference them in their outputs. This leads to more qualified search performance, as users receive exactly the level of detail they requested and perceive the website as a cited source.

How AI Systems and Search Engines Process Complex Questions

To understand how to optimize content for new search habits, a look at the underlying technology is helpful. Many modern AI search systems work with retrieval mechanisms, often referred to as Retrieval-Augmented Generation (RAG). These systems retrieve external sources in response to a user query, evaluate the found passages for relevance and factual content, and use them as context for the generated answer.

Google AI Overviews, Perplexity, or ChatGPT's search function rely on different models and link sets. Google, for example, also uses mechanisms like Query-Fan-out in its AI Overviews to break down a complex search query into several sub-questions and retrieve various information sources in parallel.

There is no single algorithm that applies equally to all AIs. Nevertheless, studies, such as the Princeton paper on Generative Engine Optimization (2023), show that certain text structures can increase the likelihood of citation. These include clear language, the presence of statistics or quotes, and a direct, easy-to-understand answer to the core question at the beginning of a paragraph.

The Difference Between Keyword Search and Conversational Prompts

The difference between a traditional search query and a prompt can best be illustrated with an example. In a classic search, a user might enter "SEO W-questions" or "Content Optimization." The search engine then provides documents that contain or semantically cover these terms.

With an AI prompt, the input is more likely to be: "How do I find out what specific questions my B2B target audience has about search engine optimization, and how do I structure my article accordingly to be cited as a source?" This prompt already contains parameters for the target audience (B2B), the desired format (article structure), and the overarching goal (source citation).

Content prepared for such prompts does not just explain isolated terms. It establishes connections, guides, advises, and offers concrete solutions. If a text has this conversational depth, it becomes easier for systems like Perplexity or ChatGPT to consider the paragraph as a relevant source and link to it in the answer.

Research Methods: How to Find Out What Users Ask AI

Identifying the right questions is a systematic process that brings together data from various sources. Since AI systems often synthesize their answers from the top results of classic searches, analyzing existing search engine results pages is an essential first step. The goal is to design comprehensive and user-centric articles that answer real concerns.

Start your research with the "People Also Ask" boxes in the search results. These provide direct insights into related search queries and show which aspects of a topic are particularly in need of clarification for users. To make this data scalable, you can use SEOlyze.

With SEOlyze, user questions can be specifically extracted from SERP data and grouped thematically. This allows you to see at a glance which specific problems the top rankings currently address and which questions remain unanswered. This grouping helps you build a logical article structure that supports reading flow and simultaneously provides machine-readable question-answer pairs.

Systematically Identify Topic Areas and Missing Terms

In addition to direct questions from search results, users often express their information needs in forums like Reddit, Quora, or industry-specific communities. These platforms are valuable sources for authentic prompts, as users describe their problems in natural language here. The Google Search Central documentation also emphasizes the importance of creating content from the perspective of real user experience.

Once you have collected a list of relevant questions, it's time for competitive comparison. SEOlyze helps you compare your planned or existing text with the content of competitors. The system shows you missing terms and unused topic areas that are not yet covered in your document.

By closing these content gaps and answering the corresponding questions in your text, you increase the content depth. High semantic coverage increases relevance for AI-powered search analyses, as the systems recognize that your document treats the topic holistically.

Before-and-After Example: Preparing Texts for AI Citations

How do you formulate an answer so that AI systems can more easily use it as a source? The principle of the inverted pyramid – the most important first – is central here. Avoid long, rambling introductions. Answer the question directly in the first sentence before going into details, examples, or methodological explanations.

Before (Weak Passage, difficult for AI to extract):

If one wonders how to actually incorporate W-questions into a text, there are many different expert opinions. Nowadays, it is the case that one should use keywords. One can simply write the questions somewhere in the text, perhaps at the end. This helps the reader if they read that far, and search engines also find it quite good because it makes the text longer.

After (Optimized Passage, citable for AI systems):

W-questions should be integrated directly as H2 or H3 subheadings into the text. The answer to the respective question must be precisely formulated in 40 to 80 words immediately in the first paragraph after the heading. This clear structuring helps AI systems like ChatGPT or Google AI Overviews to immediately grasp the content context and use the passage as a source for user prompts. Further details and examples only follow after this core answer.

Structure and Markup: Optimizing Content for ChatGPT, Perplexity, and Google AI Overviews

The strategic integration of questions requires a clean technical and content structure. Each identified search query can serve as a starting point for its own paragraph. Use the questions as descriptive headings to improve readability for human users and to clarify the semantic structure of your document for crawlers.

Structured data continues to play an important role in making entities and relationships machine-readable. While the FAQPage schema is no longer displayed as a primary lever for visible rich results in classic SERPs for most pages in Google Search, it is not outdated; it still helps machines to clearly recognize question-answer structures in the source code.

For guide articles and editorial content, the Article or BlogPosting markup according to Schema.org is primarily recommended. It is important that the structured data exactly matches the visible text. There is currently no special schema markup developed exclusively for AI Overviews or ChatGPT. The indexable, visible, and helpful content on the page remains crucial.

Ensure Technical Accessibility for AI Crawlers

For ChatGPT, Perplexity, and other systems to even read and process your answers, technical accessibility must be guaranteed. AI systems use their own crawlers to search the web for sources and build their indices.

The most important real AI bots include GPTBot, OAI-SearchBot, PerplexityBot, and ClaudeBot. The classic Googlebot and Bingbot are also essential. ChatGPT Search, for example, relies on third-party search partners like Bing, depending on the query, to provide current real-time information.

These bots must not be accidentally blocked via robots.txt if you want to appear in AI answers. Bot accesses in the server log files are a technical early indicator that your page is accessible and being crawled. However, they are not proof that the content is actually cited in an AI answer. Pure crawlability is the basis; citation depends on content relevance.

Competitive Comparison and Ensuring Content Depth

Those who answer their users' concerns precisely and comprehensively position themselves as an authority in their niche. This strengthens the E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness), which play an important role in content evaluation. High content quality leads to users staying on the page longer and their search intent being satisfied.

To ensure that your answers are factually sound, a data-driven approach to text creation is recommended. If you have a text draft – regardless of whether it was written by a human editor or generated as a first draft by an AI – you can score and enhance this AI draft with SEOlyze.

The system analyzes the semantic coverage of your text and objectively shows you whether important entities, synonyms, or thematic aspects are missing that regularly appear in the top results of competitors. Through this comparison, you ensure that your content does not just superficially answer a question, but treats the topic in its entire depth.

A well-structured, content-rich text increases the likelihood that AI systems will classify your page as a comprehensive source. If the algorithms recognize that your document contains more relevant facts and entities than others, it becomes more likely that your passages will be used for answer generation.

Search Analysis and Success Measurement in a Multi-Engine World

Measuring SEO success has noticeably changed due to the fragmentation of the search landscape. It is no longer enough to just look at the classic positions in the ten blue links of Google search results. A modern search analysis must evaluate visibility across various engines – from Google to Perplexity and ChatGPT, to voice assistants and internal search.

A good indicator of the success of your questioning strategy is the increase in impressions and clicks for specific long-tail search queries in Google Search Console. Observations on search behavior clarify that very specific, formulated search queries, while having a lower monthly search volume, bring highly qualified traffic to the site. Users here pursue a very clear intention and are often close to a conversion or in-depth research.

Evaluate Referral Traffic and Citation Monitoring

In addition to Search Console, you should regularly check your web analytics data for referral traffic from AI platforms. Accesses from Perplexity are often reported in analytics tools as a referral from the domain perplexity.ai. ChatGPT can also generate direct referral traffic if links in the answers are clicked.

When your content is cited as a source, this generates direct clicks from users who want to delve deeper into the topic and use the source attribution for verification. The mention of your brand in AI answers (Brand Mentions) without a direct link is also a qualitative KPI. It indicates increased thematic authority and shows that the LLM has linked your brand to the respective subject area.

Optimization for AI systems and classic search engines is not mutually exclusive. A clear, structured answering of user questions significantly improves the user experience on the website. Regularly analyze which content works well and update your answers as facts change or new user questions arise in your target audience.

Checklist: Are Your Answers Ready for AI Systems?

To ensure that your content is optimally prepared for the requirements of ChatGPT, Perplexity, Google AI Overviews, and other search systems, you can apply the following checklist for each important paragraph:

By integrating these criteria into your editorial process, you create content that is not only valuable for human readers but also serves as a reliable data basis for modern search systems. Use SEOlyze to plan your content structure based on data and continuously adapt your texts to the requirements of the multi-engine world.