Zhihe GrowthRetrieval-augmented generation (RAG) typically places retrieved document passages into a generative model's context so answers can use external information. Company websites may become candidate sources, but products differ in retrieval, ranking, summarization, and citation logic. Outsiders cannot know every internal weight. 'The system uses RAG' does not imply that a particular paragraph will be cited.
Observable stages from document to answer
| Stage | What the site can check | What the site alone cannot establish |
|---|---|---|
| Discovery | Robots, links, sitemap, response status | That the platform will definitely crawl it |
| Retrieval | Question relevance and search visibility | The full internal candidate-source list |
| Use in the answer | Whether the answer restates verifiable page facts | That every sentence was influenced by this page |
| Citation | Visible source card and final URL | That citation implies correct understanding |
Google's generative AI search guidance describes the use of retrieval-augmented generation and search indexes. This supports investing in search fundamentals and accessible text, but does not mean all AI systems share one architecture. For the original RAG research, see Lewis et al.; the paper's design and commercial platform implementations are not identical.
What makes a fact passage easy to verify?
'Our equipment is efficient' contains almost no reusable facts. State the model, application, metric, conditions, limits, and evidence link, such as the floor, battery, and test cycle used to obtain a value. Service pages similarly need the provider, scope, input requirements, and delivery limits. A passage read in isolation should retain its subject and must not turn 'can be assessed' into a guaranteed outcome.
Information is compressed between retrieval and generation, and models may omit qualifiers or combine numbers from different pages. Keep high-risk facts and their conditions in the same passage or adjacent table, with real links between source and case pages. Schema can describe entities but cannot repair missing conditions in visible content. Google's AI features guidance also states that no special AI Schema is required and important information should be available as text.
Why Zhihe Growth builds both knowledge and evidence resources
Zhihe Growth's Knowledge Center explains mechanisms, the FAQ Center answers narrow questions, and the Evidence Center and named case studies make key claims verifiable. SuperPDR tool selection and ELEREIN equipment procurement need product and application networks, not a generic company introduction. This architecture follows observable retrieval and buyer-verification needs; it does not claim knowledge of secret platform algorithms.
Zhihe Growth distinguishes mentions, citations, source support, and factual accuracy in acceptance reviews, using a citation-quality audit to check final URLs. Platform answers change; one round does not establish a paragraph's causal contribution. Pair high-value answer pages with original evidence, then fix unreadable body content, incorrect links, and conflicting model conditions.
An example of an error
If a source says only some models support a feature under specified conditions but AI says the whole family supports it, a citation exists without supporting the conclusion. Do not celebrate a higher citation rate. Check whether the scope is too far from the claim or another page implies family-wide support, then record the model's inference error. For capabilities, certification, and safety, support checks matter more than citation counts alone.