Table of Contents:
- How are generative search engines changing the rules of the game?
- What is GEO?
- What is text fluency in the context of GEO?
- What does fluency optimization involve?
- What does this look like in practice?
- Concrete data
- Combining strategies gives the best results
- Do SEO optimizations affect visibility in AI models?
- What does this mean for eCommerce?
Until recently, search engine algorithms based their page evaluation mainly on keywords and linking. It was enough to take care of header structure, metadata and proper phrase saturation to improve position in search results.
Currently, in the era of generative search (GEO - Generative Engine Optimization), users increasingly rarely reach a website through Google. Instead, they receive an answer generated by artificial intelligence - in ChatGPT, Perplexity or as part of AI Overview. These are responses created by language models that not only analyze data, but also interpret the meaning of content, select relevant information and independently decide which sources to cite.
It can be said that AI increasingly serves as the first shopping advisor - potential customers compare offers in the browser, check reviews or recommendations for specific products, which translates into their final purchasing decisions. It is this technological breakthrough that makes language the new field of competition for user attention. In this context, we look at the role of text fluency and how eCommerce can adapt its content to new AI model requirements to be visible to consumers.
How are generative search engines changing the rules of the game?
Generative search systems change the way users acquire information. Instead of a list of links, they present synthesized answers created based on content from multiple sources. In this model, it's no longer just the page position that counts, but the citability of its fragment.
Systems prefer content that is fact-rich and well-structured, i.e., rich in data, logical and transparent. This makes linguistic fluency have a direct impact on the chance of content being used by an AI model. Leading content tools such as Jasper, Clearscope or Surfer recommend, among others, using paragraphs of up to 80 words, clear headers and logical structure (problem > data > solution).
The importance of so-called zero-click commerce is also growing, i.e., situations when the user receives an answer without needing to go to the website. Therefore, it's worth ensuring that the content includes the brand name, key product features and language consistent with brand identification – even if the user doesn't click, they may remember the offer.
What is GEO?
GEO, i.e., Generative Engine Optimization, is a content optimization strategy for their visibility in responses generated by artificial intelligence. Unlike traditional SEO, GEO focuses not on indexing, but on text interpretation by language models.
GEO aims to increase the probability that a text fragment will be included in an AI response without the need to click on a link. This is a completely new model of competition, based on linguistic utility and content citability.
What is text fluency in the context of GEO?
In the world of GEO, it's not only what we write that matters, but how we do it. Language models such as GPT-4, Claude or Gemini perform deep semantic analysis. The evaluation includes coherence, context, clarity and logical structure of the text.
According to a study conducted by Princeton University in the USA "GEO: Generative Engine Optimization", one of the most effective strategies for increasing visibility is fluency optimization - i.e., improving the style and clarity of text without changing its content. The results? Even 30-40% increase in visibility.
You can find more detailed information in the generative search systems study.
What does fluency optimization involve?
Fluency optimization is an editorial process that adapts text to language model expectations. It includes, among others:
- simplifying sentence structures,
- eliminating unnecessary repetitions and ornaments,
- maintaining logical order (what, why, with what effect),
- using clear headers and paragraphs,
- building cause-and-effect relationships.
Such text is more "readable" for an AI model, and therefore more frequently cited.
What does this look like in practice?
To illustrate how different approaches affect visibility in generative responses, it's worth comparing two variants of product content - one optimized according to GEO principles, the other not.
Why is the content on the left GEO-friendly?
- Uses concrete, understandable language - without unnecessary "waffle" or generalities.
- Contains key product attributes as well as clear bullet points and headers.
- Guides the reader through a descriptive narrative: why it's worth it, how it works, who it's intended for.
- Complies with fluency optimization principles: logical, coherent, clear.
Why doesn't the content on the right work in GEO?
- Based on generalities and metaphors that are unreadable to AI (e.g., "feast for the skin", "immerse yourself in luxury").
- Contains no concrete data: no indication of what the product is, for whom, what ingredients it has, how it works.
- Omits keywords related to function and application.
- Lacks structure, headers, bullet points - the text becomes difficult to process.
This contrast well shows how content created "for humans" may, but doesn't have to be readable and valuable for language models. In the world of GEO, good narrative is one that combines attractiveness for the user with unambiguous informational value for AI.
Concrete data
From the previously mentioned study, it appears that adding concrete numbers such as percentages, values or statistics to content significantly increases the chance that it will be used by a language model in a generative response. This strategy, called Statistics Addition, translated into even a 41% increase in citability, i.e., the number of words from given content used by AI. Moreover, in the qualitative assessment describing how much the model considers content useful and valuable, such texts achieved even 28% better results.
Equally effective proved to be Quotation Addition - adding quotes from credible sources or unambiguous formulations increased visibility by 38-41%.
| Area | Action | Effects for AI-visibility |
|---|---|---|
| Language | Fluent and clear | +15-30% (Fluency Optimization) |
| Data | Specifics, numbers, statistics | +41% (Statistics Addition) |
| Quotes | Verifiable sources and quotes | +38-41% (Quotation Addition) |
| Traditional SEO | Keyword stuffing | Worsening results |
For eCommerce, this is a clear signal that the more concrete data a description or article contains, the greater the chance it will be cited, which is associated with the brand appearing in AI-generated responses.
Combining several strategies gives the best results
The greatest effectiveness was achieved by content that combined different approaches: was well written, contained concrete data and quotes from credible sources. Comprehensively optimized content clearly dominated in the GEO benchmark. This shows that the key is not one element, but a combination of language quality and information value.
When researchers tested content containing concrete numbers, e.g., percentages, statistics or numerical values, they noticed a clear improvement in visibility in AI-generated responses.
In practice, it looked like this: if text contained numerical data, the Perplexity.ai model cited it 37% more often. Similarly worked adding quotes from credible sources - in this case citability increased by 22%. This shows that AI more willingly uses content that is concrete, measurable and unambiguous.
Do SEO optimizations affect visibility in AI models?
Largely yes. Many proven SEO practices remain current. Page structure, technical optimization, content quality or keyword selection still matter. However, the entry of language models onto the scene has made some errors today have much more serious consequences. An example is so-called keyword stuffing, i.e., unnatural saturation of text with keywords. Previously, this already lowered page quality in the eyes of Google algorithms, but now AI models react to this even more unambiguously, omitting such content in generative responses.
What does this mean for eCommerce?
For online store owners, this is a clear signal that the bar has been raised. Today, content must simultaneously respond to the needs of users, Google algorithms and AI language models. It's no longer enough to write only for SEO or only "for users" - finding balance and taking care of every aspect of communication becomes key: from technical correctness, through clarity of message, to citability potential in generative responses.
