When people talk about GEO (Generative Engine Optimization), they often focus on how to make AI remember their product. A more useful question is: when users delegate product discovery to AI, how does AI understand the request and find the products to recommend?
You can start investigating that question with a simple three-step method.
1. Work backward from the questions users will ask AI
Do not begin with your product’s own keyword list. Start from the user’s point of view and imagine how they would describe the problem they want to solve.
They may ask questions such as:
- What tools are suitable for solving a specific problem?
- Which products fit a particular type of user or use case?
- What are the differences between these products, and which one should I choose?
Organize these questions by user goal, use case, and comparison intent. They are closer to real AI conversations than a list of product features, and they give you a better basis for deciding what content to create.
2. Ask the questions and observe how AI interprets them
Once you have a question list, give those questions to AI instead of guessing how it will respond.
Pay attention to three things:
- How AI interprets the user’s actual problem;
- Which terms it treats as search keywords, and which angles it uses to search;
- Which pages, products, and sources it cites, and why those results seem relevant.
The point is not only to check whether your product is mentioned. If it is not, the product lists, sources, and missing information in the answer can show you how AI currently understands the category and what users still need explained.
Save the exact prompt, response, cited links, and date for each test. Different models, wording, and time periods can produce different results, so records are necessary for meaningful comparisons.
3. Organize the findings and ask AI to analyze them
Turn your question-and-answer records into a structured set of notes, then ask AI to analyze them. Useful questions include:
- Which user questions keep recurring?
- Which keywords and use cases are worth covering?
- What do the cited pages have in common?
- Which definitions, comparisons, or facts are missing from our content?
- Which pages should we create next, and where should we publish them?
The analysis should lead to concrete actions: improve explanations on product pages, create articles that answer real questions, publish clear comparisons and usage guides, and distribute them where the target users are likely to find them.
This is not a one-time optimization. It is a loop: work backward, ask, summarize, and then use the new observations to work backward again.
GEO is not simply about stuffing keywords into content or forcing AI to mention a particular product. At its core, it studies what happens after users delegate product discovery to AI: how AI understands the need, searches for information, evaluates relevance, and eventually finds products. Understanding that process helps you place useful product and content information where it is more likely to be discovered.