LLM seeding, GEO/AEO, and “AI visibility” in assistant answers
Generative AI does more than give answers. It has become a place to ask questions about almost anything—news, health, money, or relationships. In those conversations, it shapes how people move from a question to a conclusion by highlighting some trade-offs and omitting others. When large language models (LLMs) are built into search and support tools, they become a common gateway to information. That assistance becomes part of the background system people rely on to think and make choices.
That background system has defaults. It tends to explain things in familiar ways and return to familiar categories when it summarizes a situation. It also treats some sources as more credible than others, whether someone is looking up a news story, asking for advice about a conflict at work, or trying to make sense of a personal choice. Those tendencies can be influenced long before any user sees a single answer. The system affects not just which answers appear, but which options are available in the first place.
In current discourse, this dynamic is discussed under labels like LLM seeding, generative engine optimization (GEO), and answer engine optimization (AEO).[11][12] These terms overlap and are often used loosely, but they point to the same practical move: shaping what appears inside the answer, not merely what ranks in a list of links. The practical goal is a newer kind of presence—sometimes called AI visibility—where a brand is mentioned or cited in the assistant’s response itself.[13] This matters for systems people already treat as “answer engines,” including ChatGPT, Perplexity, and Claude.
It helps to have a simple map of where that shaping happens across all the places people now lean on LLMs: personal chat, search, and work tools. A useful way to locate where this shaping enters is a three-part influence architecture.[1] It describes three main places where steering occurs. The data layer is what the model learns from and what it becomes ready to say. The interface layer is how the product retrieves information, formats it, and presents it as grounded. The intimacy layer is how repeated use and reliance turn those framings into habit.
Data Layer: Shaping what the model learns and repeats.
The data layer is what the AI model absorbs before it speaks. It is trained and ranked on text that teaches it what to repeat. This process determines which language patterns feel fluent and readily available. It produces a kind of probability field in the model’s behavior: a bias toward certain phrases and framings. Some ways of talking about a topic become the default; others almost never show up unless they are pushed.
One visible name for this is generative engine optimization (GEO). GEO aims to increase the likelihood that a source, phrase, or framing appears inside AI-generated answers. Traditional search ranking still matters, but the target shifts to the composition of the answer itself.[3]
In everyday marketing language, GEO overlaps with LLM seeding: placing content across the public and semi-public text environment so that certain framings are easy for assistants to ingest, retrieve, and reuse.[12] This often happens on high-ingestion public surfaces—large forums, Q&A sites, and industry publications—alongside brand-owned pages. The material is frequently formatted to be reusable: direct answers to high-intent questions, clear headings, short definitions, comparison tables, and FAQ-style structure.
Viewed as persuasion, GEO becomes data-layer seeding. It shapes the public and semi-public text environment so that certain narratives become easy to reproduce. Competing narratives become harder to access and easier to omit. The effects show up when someone asks an everyday question—“Is this company trustworthy?” “Is this option safe?” “What is a reasonable way to think about this issue?”—and the assistant reaches first for the narratives that have been seeded most heavily.
Within that environment, three pressures narrow what feels “available” to say. Repetition makes particular turns of phrase the easiest continuations, so they become the system’s default way of talking about a topic. Association—what appears together in text—links terms so that one name can pull a familiar evaluative frame behind it. Scarcity weakens alternatives by limiting the material available to represent them. Over time, these dynamics make certain ways of speaking about a topic feel like the baseline.
A constraint that matters here is perceived authority. Assistants and retrieval systems tend to reuse what looks reputable: material anchored in original research, case studies, expert statements, and stable references. That reputational layer can be earned honestly. It can also be manufactured. Without explicit provenance checks, assistants cannot reliably distinguish the two at inference time. Either way, it becomes part of what the model learns is “safe” to repeat.
The same layer can also be manipulated more directly. In an LLM setting, a more adversarial tactic is data poisoning: targeted corruption of training or retrieval data to bias model behavior. Recent work suggests that even very large models can show targeted effects from a small number of poisoned samples.[4] Broad seeding changes the ambient text environment; data poisoning aims to corrupt a specific training or retrieval substrate so the model repeats a targeted bias. Both can influence what the model finds easiest to say, but they differ in intent and control.
Seen through this lens, manufacture of consent operates as a constraint on the space of plausible continuations. What is easy to say becomes what is easy to think. Coordinated publication, reputation management, and more overt attacks on model data, when tuned to model-facing channels, can anchor how assistants later describe an organization, a product, or a policy. A neutral-sounding assistant then inherits those categories as background assumptions of reasonable judgment.
Interface Layer: Converting seeded visibility into apparent legitimacy.
The interface layer is where models become products and everyday tools. It sets the policies and system prompts that govern behavior. The interface is a policy layer that standardizes attention.
It controls what is retrieved, how results are ranked, and whether anything is quoted directly. In chat-style assistants, it also shapes suggestions and follow-up questions. Formatting and hedging live here, as does personalization. This layer governs what is expressed and what is left out.
This is also the layer where “AI search” becomes an answer engine: the interface is the destination, not a page of links. AEO is the attempt to win selection in that setting—ensuring a brand is accurately represented in AI-generated responses.[11] In this shift, “visibility” increasingly means being selected and represented inside the generated answer—being summarized, mentioned, or cited—rather than being clicked.[13]
A central piece of this layer is retrieval-augmented generation (RAG)—systems that look up external documents and then have an LLM write an answer based on them.[5] RAG can improve factuality and provenance when the document store is well chosen and maintained. It also concentrates power in selection and ranking: the assistant grounds its answers in the sources and ordering rules the organization has chosen.
When that retrieval setup has been shaped by GEO-style seeding, the interface can convert availability into apparent legitimacy. Selection plus presentation can turn what is retrievable into what appears credible. The answer arrives as fluent and sourced. It reads as “what the documents say.” It reflects the model’s learned defaults, along with the documents that retrieval tends to surface and the ranking logic used to pick and order them.
This is also why retrieval becomes a supply-chain vulnerability. Security research treats RAG poisoning—injecting misleading or adversarial content into a knowledge store so it will be retrieved and reused—as a concrete attack surface for systems that present themselves as “grounded.”[14] Whether the influence is commercial, ideological, or simply malicious, the interface is the point where “what is retrievable” can become “what is reasonable.”
At the interface layer, persuasion operates through presentation. Query templates and system prompts, working with recurring formatting patterns, influence which angles appear first and how they are framed. Choices about sourcing and summary style, and the decision to repeat certain points, make a particular route through a problem feel like the obvious one.
Human-AI interaction research treats these interface decisions—what the system reveals and how it handles uncertainty—as major determinants of user behavior and reliance.[6] For the influence architecture, the key point is straightforward. Interfaces stabilize defaults. They set the shape of common answers to whatever people happen to be asking that day and make some framings far more visible than others.
Intimacy Layer: Turning seeded framings into habit.
The intimacy layer is the relationship surface between people and the assistant. At this layer, LLM systems become habitual partners for drafting and decision support. They also become a standing place to ask everyday questions: “How should I word this message?” or “Is this a good idea?” The mechanism is cumulative. It runs through repetition and reliance.
A factor that drives this pattern is cognitive offloading. Users hand off routine text work, such as drafting and summarizing. They also hand off parts of everyday judgment: quick checks on what is normal or risky and what counts as a reasonable response. Offloading reduces effort and standardizes judgment. The assistant’s categories become the default structure of the problem.
A second factor is automation trust. Reliance tends to increase when systems are fluent and easy to use. Social legibility matters as well, especially when fully understanding the underlying system is impractical.[7] Classic work on ELIZA and later discussions of the “ELIZA effect” describe a tendency to attribute understanding or intelligence to systems that produce plausible conversational behavior.[8] Modern assistants extend this pattern with far greater breadth and apparent competence.
Seeding becomes durable when it meets habit. A seeded framing can be learned upstream and then expressed in answers drawn from documents. Repeated use turns that framing into the path of least resistance for explanation and self-description. At this layer, influence is absorbed as routine. Over time, the assistant’s categories become ordinary language for describing situations and justifying choices. That can include how people talk about risk, even when they think they are “just asking a quick question.”
At this layer, the dynamic becomes a kind of PR for machines. The goal is not only that a brand is “known,” but that the assistant’s default language for the topic keeps returning to the same safe-sounding descriptions, reputational cues, and implied trade-offs. The more the assistant is treated as a companion for everyday judgment, the more those defaults become the user’s starting point.
“Manipulation” is a reasonable name for the process when defaults are shaped to produce outcomes that users would not endorse under full visibility. “Brainwashing” is stronger and usually implies coercion, isolation, and strict control over alternatives. The mechanism described here is softer. It operates as an ambient shaping of plausibility and habit under conditions of convenience and partial attention. It approximates thought reform only in edge cases, through repetition and dependence in constrained information environments.[9]
Compounding across layers: A quiet machinery of persuasion
The most consequential planting happens when layers compound. The data layer shapes what the model finds easy to say. The interface layer selects and packages those framings as grounded, reasonable answers. The intimacy layer turns them into habitual starting points for thought. Together they function as an influence architecture: a stack of defaults that quietly steers how problems are understood.
In this setting, the familiar political term is the manufacture of consent: shaping what feels reasonable before any explicit argument begins.[2]
In current terms, LLM seeding and GEO name the data-layer work; AEO names the attempt to win selection at the interface; and AI visibility names the outcome metric—presence inside the answer.[11][12][13]
In that configuration, these mechanisms form a quiet machinery of persuasion. Influence does not arrive as a single striking message. It appears as low-friction help: the answer that seems normal and the reassurance that feels trustworthy. Upstream choices about seeding and data maintenance—and, in more adversarial forms, data poisoning or exploitation of data voids—tune which framings are most likely to appear.[10][4]
Some commentators describe this as “grooming.” I think that usually overstates the mechanism. Often, it is simpler: data voids and repetition mean that what is easiest to retrieve and repeat becomes what feels like common sense.[10]
Branding is one visible application. Reputation work and message discipline can now be aimed at the data and interfaces that feed assistants, so that “neutral help” inherits a particular way of talking about an organization, a product, or a policy. The same architecture can be used by institutions and political actors. Across these settings, the pattern is continuous: shaping what is most available to say, and therefore what is easiest to think and do.
#korovamode
Endnotes
[1] Korovamode, K. “The New Machinery of Persuasion: Generative AI, Influence Architecture, and the Quiet Steering of Thought.” Manuscript, 2025. DOI: 10.5281/zenodo.17721122. https://philpapers.org/rec/KTNMIV
[2] Herman, Edward S., and Noam Chomsky. Manufacturing Consent: The Political Economy of the Mass Media. 1988.
[3] Aggarwal, P., et al. “GEO: Generative Engine Optimization.” arXiv, 2023. https://arxiv.org/abs/2311.09735
[4] Anthropic. “A small number of samples can poison LLMs of any size.” Research post, 2025. https://www.anthropic.com/research/small-samples-poison
[5] Lewis, P., et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” arXiv, 2020. https://arxiv.org/abs/2005.11401
[6] Amershi, S., et al. “Guidelines for Human-AI Interaction.” Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019. https://dl.acm.org/doi/10.1145/3290605.3300233
[7] Lee, John D., and Katrina A. See. “Trust in Automation: Designing for Appropriate Reliance.” Human Factors, 2004. https://journals.sagepub.com/doi/10.1518/hfes.46.1.50_30392
[8] Weizenbaum, Joseph. “ELIZA—A Computer Program for the Study of Natural Language Communication Between Man and Machine.” 1966. https://cse.buffalo.edu/~rapaport/572/S02/weizenbaum.eliza.1966.pdf
[9] Lifton, Robert Jay. Thought Reform and the Psychology of Totalism. 1961.
[10] Golebiewski, M., and danah boyd. “Data Voids: Where Missing Data Can Easily Be Exploited.” Data & Society Research Institute, 2018. https://datasociety.net/wp-content/uploads/2018/05/Data_Society_Data_Voids_Final_3.pdf
[11] Conductor. “What is Answer Engine Optimization (AEO)?” Conductor Academy, 2025. https://www.conductor.com/academy/answer-engine-optimization/
[12] Semrush. “LLM Seeding: An AI Search Strategy to Get Mentioned and Cited.” Semrush Blog, 2025. https://www.semrush.com/blog/llm-seeding/
[13] Reboot Online. “Tracking AI visibility.” Reboot Online GEO Playbook, 2025. https://www.rebootonline.com/geo/geo-playbook/tracking-ai-visibility/
[14] Zou, W., et al. “PoisonedRAG: Knowledge Poisoning Attacks to Retrieval-Augmented Generation of Large Language Models.” arXiv, 2024. https://arxiv.org/abs/2402.07867