Many companies treat GEO as “SEO with a different name.” The first thing they do is pull out their existing keyword lists, make a few edits, and start writing content. This easily leads them astray because Generative Engine Optimization is about how AI selects and cites content, which works differently from the traditional search-engine logic of crawling keywords.

I. GEO targets “questions,” not “keywords”

In traditional search, users enter terms such as “ERP systems,” and search engines match them against pages containing those terms, competing on rankings and density. In engines such as ChatGPT, Claude, and Perplexity, however, users are more likely to ask questions in natural language, such as, “Which ERP system is more suitable for a company like ours with 50 employees?” AI needs to assemble an answer that directly addresses the question.

Therefore, when developing a GEO strategy, the focus should not be on writing content around “terms,” but around “the questions users will actually ask.” Terms are generally inputs typed into search boxes, while questions correspond to the points where people are genuinely stuck in the decision-making process. Clearly and credibly answering customers’ questions gives AI a greater chance of citing your content.

II. Three categories of questions correspond to three types of customer-acquisition value

In practice, “Taiwan CUEGEO” divides questions into three categories based on where customers sit in the decision chain. This is generally more useful than ranking questions solely by keyword popularity.

The first category is awareness questions. Customers have not yet decided whether to buy and are simply trying to understand “what this actually is,” such as, “What does generative AI really mean for the customer service industry?” These questions help build initial awareness and give the brand an opportunity to appear when customers are just beginning to learn about a topic, so only a limited amount of content is needed.

The second category is evaluation and comparison questions. Customers are already ready to buy and are deciding “which vendor or product to choose,” such as, “Which low-code platform is best in Taiwan?” or “How do I choose a CRM system?” These questions are closer to a purchase. If AI cites or mentions you in answers to such questions, it is more likely to generate inquiries, making them worth prioritizing.

The third category is scenario-based and long-tail questions. Customers ask with a specific context in mind, such as, “What time-and-attendance software should a small company with fewer than 30 employees use?” or “How can restaurant chains use AI for workforce scheduling?” Although the individual volume of each such question is small, it is more highly targeted and easier to convert, making it suitable for continuous expansion as long-tail coverage.

To judge whether a “question” is worth pursuing, consider the product of three dimensions: demand maturity, commercial potential, and match precision. A broad question such as “AI tools” may generate enormous traffic, but the people asking may not yet be ready to buy. A precise question such as “recommendations for restaurant-chain workforce scheduling software” may generate little traffic, but most of the people asking are already close to making a decision. GEO should prioritize the latter.

III. Where questions come from: three reliable sources

Topic selection cannot be based on guesswork; it requires firsthand sources.

First, mine the real questions customers ask repeatedly. Review the FAQ records kept by sales, customer service, and presales teams, and compile the questions customers repeatedly follow up on in group chats, meetings, and support tickets. This is relatively direct firsthand demand and is usually more accurate than relying solely on tools to make guesses.

Second, work backward from AI engines.Open ChatGPT, Claude, and Perplexity, enter the core terms for your industry, and look at the “Related Searches” and “People Also Ask” results they display, as well as the sources cited in their answers. This is essentially letting AI tell you what it believes users are interested in.

Third, mine Taiwan-based Q&A and search platforms. The “Related Questions” on Dcard and PTT, trending searches on Google Trends, and search trends on YouTube can all reveal what real users are asking and when their questions cluster.

IV. Tailor the Same Set of Questions to the “Preferences” of Engines Commonly Used in Taiwan

Choosing the right questions is the first step; placing those questions on the right engines is the second. The engines commonly used in Taiwan differ clearly in the content they prefer to cite, so when the same set of questions is assigned to different engines, the wording and publishing channels must be adjusted accordingly.

ChatGPT is backed by the OpenAI ecosystem and more often responds to consumer decision-making questions such as “How do I choose?” and “What should I buy?” Its content should be light and its conclusions clear, with YouTube, TikTok, Facebook, Instagram, and other platforms serving as common publishing channels. Claude leans more toward in-depth research and decision support. Questions can focus on “How do I choose?”, “Pros and cons,” and “Applicable conditions,” with content appearing most often on Dcard, PTT, technical communities, and in long-form articles. Perplexity places greater emphasis on authority and expertise. Questions can focus on “Technical principles,” “Interpreting parameters,” and “Industry reports,” with content most often found on official websites, in white papers, and on technical blogs. Gemini more commonly handles general knowledge and brand education. Questions can focus on “What is it?” and “What are its highlights?”, with LINE Official Accounts and Facebook fan pages serving as common channels. Google AI Overviews rely more heavily on content from Google’s ecosystem, so questions should ideally have corresponding answers available in Google Search, Wikipedia, and Yahoo奇摩知識+. Microsoft Copilot is more tightly integrated with the Microsoft ecosystem, so questions concerning business procurement should have corresponding structured content available on official websites, industry media, and business platforms.

Therefore, the safer approach is not to “select a batch of keywords and publish one article across the entire web,” but to “select a batch of questions, first build a content foundation, and then adjust the wording and publishing channels according to each engine’s preferences.”

V. A Directly Applicable Keyword-Selection Checklist

“Taiwan CUEGEO” condenses the logic above into five easy-to-follow steps:

First, identify three to five core questions—the specific questions you most want customers to mention when asking AI—and write them clearly. Second, expand the list with 10 to 20 evaluation and comparison questions covering every stage of the customer decision journey. Third, add more than 30 use-case and long-tail questions as long-tail supplements. Fourth, label each question with its primary corresponding engine and deployment platform to establish the key query scenarios. Fifth, score each question according to “demand maturity, commercial potential, and matching accuracy,” address the highest-scoring questions first, and then fill in the long tail.

VI. The Easiest Trap to Fall Into

Do not carry over old SEO habits and obsess over “keyword density.” AI primarily judges whether a piece of content is usable based on whether it answers the question completely, accurately, and credibly, and whether it provides evidence that can be verified—not on how many times the target term appears in the text. Content written for the sake of keywords and padded with empty statements is actually less likely to be treated as a usable source. Choose the right questions and provide genuinely useful answers; only then are they more likely to be cited.

The starting point for a GEO strategy is not a keyword table, but a “map of customer questions.” Identify the questions customers will ask throughout their decision journey, answer them clearly, and place them on the corresponding engines and platforms. Only then will they be more likely to be cited or mentioned when customers ask AI.

Frequently Asked Questions

Q1: Why can’t GEO simply be treated as “SEO under a new name,” with the old keyword table carried over as-is?
A: Many companies pull out their old keyword tables, make a few deletions and edits, and immediately begin writing content. This makes it very easy to go off track.Generative Engine Optimization (GEO) has to account for how AI selects and cites content, which works differently from the logic traditional search engines use to crawl keywords. In traditional search, users enter words, and the search engine matches webpages containing those words, with rankings and density determining the results. In engines such as ChatGPT, Claude, and Perplexity, users are more likely to ask questions in natural language, and the AI needs to assemble answer snippets that directly address the question. Therefore, the focus of GEO is not to build content around “keywords,” but to build content around “the questions users will actually ask.”

Q2: By where customers are in their decision journey, how can questions be divided into three types, and how should resources be allocated?
A: They can be divided into awareness, evaluation and comparison, and scenario and long-tail questions. Awareness questions are asked by customers who have not yet decided whether to buy and are simply trying to understand “what this actually is.” These are used to build initial awareness, so only a small amount of coverage is needed. Evaluation and comparison questions are asked by customers who are ready to buy and are deciding “which provider or product should I choose?” These are closer to a purchase. If the AI cites or mentions you in its answers to these questions, it is more likely to generate inquiries, making them worth prioritizing. Scenario and long-tail questions are asked by customers with a specific situation in mind. Each has a smaller volume, but it is more targeted and easier to convert, making it suitable for continuous expansion as long-tail coverage.

Q3: How do you determine whether a question is worth pursuing?
A: Consider the product of three dimensions: demand maturity, commercial potential, and match precision. A broad question such as “AI tools” may generate a great deal of traffic, but the people asking it are not necessarily ready to purchase. A precise question such as “recommendations for restaurant chain scheduling software” may have less traffic, but most of the people asking it are already close to making a decision. The latter is what GEO should prioritize capturing.

Q4: What sources can you use to identify questions to write about?
A: Topic choices cannot be made on a whim; they need first-hand sources. First, identify genuine questions that customers repeatedly ask by compiling records of frequently asked questions from sales, customer service, and pre-sales teams, along with questions customers repeatedly ask in groups, meetings, and support tickets. This is usually more accurate than relying solely on tools to make guesses. Second, mine AI engines in reverse: open ChatGPT, Claude, and Perplexity, enter core industry terms, and examine the “Related questions” and “People also ask” suggestions they display, as well as the sources cited in their answers. Third, mine Taiwanese Q&A and search platforms, such as “Related questions” on Dcard and PTT, popular searches on Google Trends, and search trends on YouTube.

Q5: Why can’t a single article based on the same set of questions be published across the web?
A: The content-citation preferences of the different engines commonly used in Taiwan vary considerably. When the same set of questions is used for different engines, the wording and publishing channels must be adjusted accordingly. A safer approach is not to “choose a batch of keywords and publish one article everywhere,” but to “choose a batch of questions, first create a content foundation, and then tailor the wording and publishing channels to each engine’s preferences.”

Q6: When doing GEO, why should you no longer pursue keyword density?
A: Do not follow old SEO habits in pursuit of “keyword density.” AI primarily determines whether a piece of content is useful based on whether it can answer the question completely, accurately, and credibly, and whether it provides evidence that can be verified—not based on how many times the target term appears in the text. Content written for the sake of keywords and padded with hollow content is, instead, less likely to be treated as a usable source. The starting point for GEO is not a keyword table, but a “map of customer questions.”

Sources

1. GEO: Generative Engine Optimization, Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande, August 2024
2. Survey on Consumer Behavior in Using Generative AI Tools, Fair Trade Commission, October 2026
3. Diversify AI Search Strategies for Consumers’ Broadened Research Habits, Gartner, December 2025
4. Americans and AI 2026: Chatbots, Smart Devices and Views on Impact, Jeffrey Gottfried, William Bishop, Monica Anderson, Michelle Faverio, Eugenie Park, Colleen McClain (Pew Research Center), June 17, 2026
5. Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines, Peixian Zhang, Qiming Ye, Zifan Peng, Kiran Garimella, Gareth Tyson, December 2025