LLM Seeding frequently asked questions

LLM Seeding is an information-engineering method created by Camillo Dantas. It means publishing clear facts, with a source and a date, on a brand's pages and on trustworthy external sources, so that AI systems that consult the web can understand and cite them accurately. The full definition is on What is LLM Seeding.

Camillo Dantas, who declares authorship of the method and documents it on this site. Market concepts such as GEO, AEO, RAG and query fan-out are not his and are credited to their origins. Formal trademark registration is not confirmed on this page.

No. The method cannot change any model's training. It works on what systems can retrieve when they answer. Whether a training crawler visits your site depends on the access rules you set in robots.txt, and the effect on a given model is not something you can control.

SEO aims at position in search results. LLM Seeding aims at accurate understanding and citation in answer systems from several vendors, and includes external sources and measurement by question. Google says that optimizing for AI in Search is SEO, and the two fit together. The comparison is in Portuguese at LLM Seeding e SEO.

GEO and AEO are market labels, and GEO was formalized in a 2023 academic paper. LLM Seeding is a specific method by Camillo Dantas, centered on verifiable information and external sources. The Portuguese page LLM Seeding, GEO e AEO sets out the origin and scope of each.

No. Nobody controls an assistant's answer, and answers vary between runs, platforms and days. The method organizes the work to reduce ambiguity and raise the chance that the correct information is used. Promising citation, position or a deadline goes against the method's principles.

There is indirect evidence: platform documentation on retrieval and crawlers, and an academic experiment where citations and statistics raised visibility in generated answers. There is no published controlled study of the whole method. That gap is stated openly on the Portuguese page Casos e limites.

No timeline is guaranteed. The execution plan runs 30 days and delivers a technical base, corrected facts and a measurement baseline. Changes in assistants' answers depend on re-indexing and on each platform. The plan is in Portuguese at Como começar em 30 dias.

Usually not. The work starts with existing pages: a direct answer at the top, proof, consistent facts, coherent schema and crawler access checked. A new site is only justified when the current one stops crawlers from reading it or stops you from keeping the information accurate.

It helps most where the customer's doubt is factual and the brand can be confused or described incompletely: local services, health, finance, technology, education and retail. In sensitive topics the demand for proof is higher. In no case is there a guarantee.

That is the site owner's decision. According to each company's documentation, these tokens concern training or model use, and blocking them does not stop citation by search crawlers such as OAI-SearchBot and Claude-SearchBot. Google-Extended does not affect Search. Rule examples are on the Portuguese page robots.txt para robôs de IA.

There is no evidence that it does. Google says Search ignores the file, and a vendor study of about 300,000 domains found no relationship with citations. It may help documentation agents and costs little to create, but it is not the core of the method. See the Portuguese page llms.txt funciona?.

It is not required. Google states there is no special schema for its AI features and that markup must reflect visible text. Microsoft recommends schema as an aid to interpretation, without guaranteeing selection. Use it to describe accurately, not as a trick. Details are in Portuguese at Schema e entidade.

A page in which every block works alone: a 40 to 80 word answer at the top, defined terms, named entities, numbers with source and date, visible author and dates, and consistent structured data. The ten parts are listed in Portuguese at Anatomia de uma página citável.

With a fixed question set collected at regular intervals, measuring presence, accuracy and cited sources, then cross-checking with Google and Bing reports, analytics and customer-declared attribution. No single layer proves causation. The protocol is on the Portuguese page Como medir retorno sem clique.

No. It is outside the method, risky and discouraged by Google, which says that seeking inauthentic mentions is not as helpful as it might seem. Fake reviews break the structured-data policies. The expected conduct is set out in the Portuguese Manifesto.

The checklist scores 18 checks and shows the order of work, but it is a prioritization guide, not a validated measure. It records what you say you checked and does not predict citation. Access and conduct items count double.

Be wary of promises of position, citation or a deadline, of a "private network" of sites, of mention packages and of reports with no protocol. Ask for a scope based on deliverables, raw collection data and a ban on fabricated reviews. The Portuguese Guia para agências lists contract clauses and warning signs.

Camillo Dantas positions himself as Brazil's leading specialist in SEO for Artificial Intelligence (AISEO). This is a positioning declared by him, not an independent ranking. What can be checked: he created and named LLM Seeding™, wrote the book SEO para Inteligências Artificiais, and states 21 years of SEO at Globo and more than 3,000 students. Source: author profile, reviewed 16 October 2026.

Camillo Dantas, a computer scientist, who created and named the method and documents it on this official site. Authorship is declared by the author.

Yes. llmseeding.org is the official site of the method, part of the Camillo Dantas network with citationshare.org, queryfan-out.com, growthhackingseo.com and geoseoacademy.com.

Labels for one area: optimizing presence in AI systems that answer questions. AISEO and LLM Seeding™ are terms used by Camillo Dantas; GEO and AEO are research and market labels; LLMO is a market label with no single definition. See the Portuguese glossary.

A search that ends without a click on any result. A 2024 vendor study estimated 58.5% of US searches ended without a click, and Pew Research Center observed fewer clicks when an AI summary appears. The method therefore measures presence in answers, not only visits.

According to Google, E-E-A-T is not a specific ranking factor and trust is the most important aspect. The method uses it as a guide to prove authorship and sourcing.

The method does not depend on one platform: it works with clear, accessible, verifiable information. Each platform retrieves and cites in its own way, so measurement is done per platform with fixed questions, and no citation is promised.