The short answer
A company website can describe its product in detail and still be absent from an AI answer. The page may never have been retrieved. Or the material supplied to the model may have lacked the information needed to answer the question. Understanding how text is processed helps separate these problems.
This article draws on research into Transformers and retrieval-augmented generation, alongside public guidance from Google and OpenAI. It focuses on what website owners can reasonably learn from those sources. Model architecture does not, by itself, reveal a platform's ranking rules.
Generative engine optimization, or GEO, concerns visibility in AI-generated answers. For a business website, a useful goal is to make public information easier to discover and use accurately. That involves both the model processing the text and the system selecting which text the model receives.
From text to tokens and context
A tokenizer converts text into small units called tokens and maps them to numeric identifiers. A token can represent a word or part of a word. The boundaries depend on the tokenizer, so a word in English or a character in Chinese does not always correspond to one token. Hugging Face tokenization documentation
The model converts these identifiers into numerical representations. Within a Transformer, attention combines information from different positions using calculated weights. This helps representations incorporate context. Original Transformer paper
Consider this illustrative sentence.
The document assistant accepts PDFs, but scanned files require text recognition first.
An accurate interpretation needs to preserve the relationship between scanned files and the text-recognition requirement. Retaining only “accepts PDFs” loses a condition that could matter to a buyer.
The editorial implication is straightforward. Keep limitations close to the capability they qualify, and make the subject clear. These are suggestions for reducing ambiguity. They do not establish that a particular sentence structure receives more model attention or earns more citations.
Tokens, paragraphs, and retrieved passages are different things. A token is a model input unit. A paragraph organizes writing. A retrieval system may select passages using its own process. There is no fixed one-to-one mapping between them.
The model may receive only part of your page
Before asking how a model interprets a page, ask what material actually reaches it.
Retrieval-augmented generation, commonly shortened to RAG, combines retrieved information with a generative model. The original RAG research explored using retrieved text passages to support generation. A model can therefore use information supplied for the current task without that information first becoming part of its training. Original RAG paper
This simplified sequence illustrates one common approach. It is not a specification for every commercial platform.
Web pages → Acquisition and processing → Searchable material
Question + Searchable material → Retrieval → Model receives context → AnswerGoogle says AI Overviews and AI Mode may expand a question into related searches to find supporting pages. A response can consequently draw on material from several pages. Google's explanation of AI search features
Write with partial reading in mind. If a passage is encountered on its own, can a reader identify the product? Are the conditions of a claim still present? Is there evidence nearby that supports it?
A useful editing exercise is to copy an important passage into an empty document. Read it without the earlier brand introduction. If “it,” “this solution,” or “the capability above” becomes ambiguous, restore the necessary name or context.
This does not require splitting an article into tiny sections. Google explicitly rejects a universal ideal length or mandatory “chunking” for AI search. Its guidance also says that special Schema markup and llms.txt are not requirements for its generative search features. That guidance applies to Google; other systems need to be assessed separately. Google's generative search optimization guide
Semantic similarity helps explain relevance
Retrieval can use numerical representations of meaning, often called semantic embeddings. Text is converted into vectors that can be compared for similarity. Sentence-BERT demonstrated a method for producing sentence embeddings suitable for such comparisons. Sentence-BERT research
This illustrates a technical capability, not the undisclosed implementation of every AI search product. Commercial systems may combine several retrieval and ranking methods.
For an editor, the practical question is whether the page contains information that answers a buyer's question, even when the buyer uses different wording.
The following copy is fictional and exists only to demonstrate an edit. It describes neither SolveReal Systems nor a real customer deployment.
This tool offers powerful document processing for a wide range of business needs.
A more useful description might read as follows.
Example Document Assistant helps purchasing teams organize supplier quotations. It extracts product names, quantities, and prices from PDFs with a text layer and exports them to a spreadsheet. Scanned files need text recognition first. Purchasing staff must check the exported results, and the tool does not send purchase orders.
The revised passage gives readers information they can use to evaluate the product. They can assess quotation handling, document requirements, and the remaining human work. Whether the edit improves retrieval or citations on a particular platform requires observation. Semantic similarity alone cannot establish that outcome.
More text cannot substitute for clearer evidence
A model has a limit on the input it can process at once, commonly called its context window. A larger allowance does not ensure equally reliable use of every detail.
The 2023 study Lost in the Middle found that the location of relevant information affected performance in the models and tasks it tested, with weaker performance when relevant information appeared in the middle of long inputs. It does not establish identical behavior in every current model, nor does it prove that placing a brand near the top of a page earns recommendations. Long-context research
A sensible editorial response is to keep conclusions close to their conditions. When reporting a test result, identify the sample and test date nearby, and link to evidence that supports the result. A qualification that changes a purchasing decision should be easy to find alongside the claim.
Citations deserve the same care. A link gives readers a place to check; it does not automatically substantiate the sentence beside it. General research about a model's capabilities cannot establish the performance of a particular product.
Review the article's most consequential factual claims individually. Open the supporting sources and compare their actual scope with the wording on the page. Narrow claims that exceed the evidence, and remove or substantiate unsupported statements.
Boundaries
This guide covers public website content and technical checks. The following steps help identify issues; they do not guarantee indexing, citations, or business outcomes.
Practical steps
Check access, then observe how answers use the content.
These improvements depend on the target system obtaining the text. Google's JavaScript guidance explains that some sites require script execution before their main content appears, and that not every bot can run JavaScript. Making essential information available in the initial HTML reduces that dependency. Google's JavaScript documentation
Compare the page response with what appears in the browser. Look beyond titles and navigation to the actual explanation and the limitations inside tables. A complete browser view alone does not establish what a target crawler received.
Access controls also serve different purposes. OpenAI separates OAI-SearchBot, used for search, from GPTBot, which collects content that may be used for training. Allowing search access does not require allowing training collection, and permission to crawl does not guarantee citation. OpenAI crawler documentation
Verification checklist
To evaluate changes, start with a fixed set of real buyer questions. Record the platform, language, search mode, and test time. Save the answer and the URLs actually cited. Test Chinese and English separately.
Keep three observations distinct. Did the website appear among the sources? Was it described accurately? Did people continue to the website? One mention after an edit is one observation. Repeated checks under comparable conditions provide a stronger basis for discussing whether a change is consistent.
Sources
Research papers and official documentation are linked beside the relevant claims. Sources were checked on September 4, 2026. Consult the current documentation when platform rules change.
Next step
To begin with an existing website, Open GEO Console can help organize issues involving access, public content, and citation evidence for the site owner to review. Choose one important service page, identify the facts it lacks for a buyer's decision, and address those gaps before attempting a site-wide rewrite.
You can also read the Open GEO project overview, explore our enterprise AI services, or contact us with your website and buyer questions.