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OpenAI DeepResearch on AI Optimization and optimization methodologies. Ran on April 11, 2025, completion time: 26m with 5 sources.

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How LLM Applications Choose and Reference Content

Large Language Model (LLM) applications like ChatGPT, Perplexity, Claude, and Google’s Search Generative Experience (SGE) use a mix of pre-trained knowledge and real-time retrieval to answer queries. Understanding how these systems select and cite content is the first step to improving brand visibility. Generally, modern generative engine pipelines follow a two-step process: (1) retrieve relevant documents (often via a search engine or internal database), and (2) have an LLM generate a synthesized answer grounded in those sources. For example, a query is broken into simpler search terms, top results are fetched (e.g. top ~5 pages), and then a model like GPT-3.5/GPT-4 composes an answer using that material. This design is used by systems like Bing Chat and Perplexity.ai and underpins Google’s AI Overviews as well. It ensures the answer remains grounded in real content with attribution for verification (GEO.pdf).

Because of this pipeline, source visibility in LLM answers depends on both traditional search ranking and content usefulness after retrieval. If your site isn’t among the top results fetched, the LLM can’t even consider it. Once retrieved, the LLM’s selection of what to include (and cite) depends on factors like relevance, authority, and how the information is presented in the text (82% of Google AI Overviews citations come from deep pages: Report). Unlike a classic search results page (10 blue links), a generative answer is a blended narrative – it might pull a key fact from one source, a quote from another, etc., rather than just highlighting the first result. This means even a lower-ranked page can surface in the answer if it contains a unique piece of value (e.g. a statistic, a definition, an expert quote) that enriches the answer. In fact, experiments show that optimizing content can boost a lower-ranked site’s inclusion significantly – for instance, adding citations to a page led to a ~115% increase in its visibility when it was originally the #5 search result (while the top result’s share decreased). In summary, LLMs determine content visibility by balancing relevance (does the content address the query directly?), authority (is the source trustworthy?), and contribution (does it add something unique or verifiable to the answer?).

Factors Influencing Source Visibility in LLM Responses

• Relevance and Query Alignment: LLM applications heavily favor content that directly answers the question or aligns with the user’s intent. Pages that clearly cover the question’s topic and use related keywords in context are more likely to be fetched and used. However, simply stuffing keywords is ineffective – a study found that traditional SEO tricks like keyword stuffing actually reduced visibility (performing ~10% worse than no optimization). Instead, content should match the query intent in a natural way (e.g. a page title, headers, or opening sentences that mirror the user’s question). Structuring content in a Q&A format or with headings that anticipate common queries can help LLMs quickly identify relevant sections. Remember, the generative system often skims for the part of your page that answers the question – clear sections (like a “Key Takeaways” box or an FAQ section) can make your content more extractable.
• Authority and Accuracy: LLMs and their retrieval components prefer authoritative sources for factual information (Ziff Davis's Study Reveals That LLMs Favor High DA Websites - Moz). A site’s overall authority (e.g. credible domains like major news outlets, recognized expert blogs, Wikipedia) increases the chance that its content will be chosen or cited. Google’s AI Overviews, for instance, overwhelmingly cite “deep” content pages on reputable sites rather than homepages (82% of Google AI Overviews citations come from deep pages: Report). In one analysis, 82.5% of AI Overview citations were to internal pages (two+ clicks from the homepage) containing detailed content, whereas only 0.5% cited a website’s homepage (82% of Google AI Overviews citations come from deep pages: Report). This indicates the AI values specific, information-rich pages over general fronts. Domain Authority (a proxy for a site’s backlink profile and trust) also correlates with inclusion in LLM training and outputs: recent research shows LLM training datasets skew heavily toward high-DA websites, far beyond their share of the web (Ziff Davis's Study Reveals That LLMs Favor High DA Websites - Moz). In fact, key LLM corpora like OpenWebText2 contain a much higher proportion of high-authority publisher content (news, reference sites) compared to uncurated web data (Ziff Davis's Study Reveals That LLMs Favor High DA Websites - Moz). Major LLM developers explicitly prioritized “high-quality content owned by commercial publishers” in their training mixes. The practical takeaway is that content coming from a source that the AI recognizes as reliable (either because it’s in the training data or because the search algorithm ranks it highly) will have a better shot at being referenced. Ensuring factual accuracy and citing your sources can further boost credibility – generative models tend to include content that they can support with a citation. If your page makes a claim and already backs it up with an external reference, an LLM sees both the information and a built-in source to cite, making its job easier.
• Content Depth and Uniqueness: Generative answers often aggregate information, so offering unique value increases your inclusion. Content that provides a unique angle – e.g. proprietary data, a notable quote, a fresh statistic, a case study – can make your brand’s page stand out among sources. The GEO research paper calls this “Statistics Addition” and “Quotation Addition” – and found both markedly improve visibility in answers. By adding relevant statistics or expert quotes, a site becomes the origin of a memorable fact or statement the LLM might want to include. In GEO’s benchmarks, adding statistics to content yielded ~26–34% higher subjective visibility scores, and adding a quotation from an expert drove ~32% higher scores. The presence of concrete data points not only enriches the answer for the user but also signals to the AI that the content is informative. Similarly, emphasizing key points (through formatting like bold or by writing in a persuasive, authoritative tone) can increase a source’s impact. In qualitative examples, simply adding the source name of a statement or emphasizing a critical insight in the text boosted that source’s appearance in the final answer. In short, content that is comprehensive, contains verifiable facts, and offers something distinct (not just generic filler) tends to get picked up. On the other hand, fluffy or generic content might be passed over in favor of a page that, say, lists specific pros/cons or statistics relevant to the query.
• Clarity and Readability: The easier your content is to parse, the more likely an LLM will use it correctly. Strategies like “Easy-to-Understand” (simplifying language) and “Fluency Optimization” (ensuring the text reads well) also improved source visibility in experiments. Complex jargon or long-winded paragraphs might confuse the model or dilute the key information. If the LLM has to choose between two sources saying the same thing, it may favor the one that’s more succinct or clearly phrased. In fact, using concise, conversational language was associated with roughly a 20% visibility improvement in the GEO study. This doesn’t mean dumbing down your content — rather, it means clear structure, plain language for non-obvious concepts, and avoiding irrelevant tangents. Writing in a way that a layperson (or an AI) can easily summarize will help the model extract your key messages for the answer.
• Timeliness and Freshness: Especially for Google’s AI Overviews and Bing Chat, which fetch current information, having up-to-date content is important. Google’s SGE has been observed citing content that is very fresh (sometimes indicating “[Updated DATE]” in the overview). Ensuring your content is updated with recent information (and dates, where appropriate) can make it more attractive for queries where freshness matters (e.g. “best smartphones 2025”). Additionally, LLMs using retrieval may favor recently indexed content for trending queries. Make it a practice to refresh stats, revisit comparisons, and keep your last modified dates current for key pages – this signals relevance. In contrast, outdated content might be ignored if the AI finds a newer source addressing the same question.
• Training Data Presence: Aside from on-the-fly retrieval, consider the base knowledge of models like ChatGPT (GPT-4) or Claude. These models were trained on vast text corpora up to a certain cutoff (e.g., GPT-4’s knowledge is through 2021, and Claude 2’s through early 2023). They have “read” a lot of the internet, but not uniformly. As noted, LLM training favored high-quality publisher content, which means if your brand or product is mentioned frequently by high-authority sites, it’s far more likely to appear in the model’s internal knowledge. For example, if your product was reviewed in PCMag or featured in TechCrunch, ChatGPT may recall some of those details when asked – whereas if the only place your product is described is your own small blog, the model might not have included it in training. Leading models also used filtered datasets like C4 (which removed very short or low-quality pages) and OpenWebText (which included content linked on Reddit with a score threshold) (Ziff Davis's Study Reveals That LLMs Favor High DA Websites - Moz). This implies thin pages or less popular content likely got filtered out. Ensuring your brand is covered in well-regarded publications, or at least that your site’s content meets a high quality bar, increases the odds it was in the training data. That said, the newest generative search (e.g. SGE) bypasses some training limitations by pulling live data. So there’s a dual approach to visibility: one part SEO (so that the retrieval finds you today), one part digital PR (so that you were part of the model’s learned knowledge yesterday). In the special case of ChatGPT’s standard mode (without browsing), it will only reference what it “knows” from training. For your brand to be mentioned by ChatGPT unprompted, it essentially needs to be somewhat notable or well-documented online prior to 2022. This is where having a Wikipedia page or being in widely-cited datasets becomes invaluable.

Citation Behavior

Each platform has slightly different citation behaviors that influence how visibility manifests:

• ChatGPT (standard) doesn’t provide citations, and will only mention sources if asked or if the information is strongly associated with a source in its training. It might say “According to [Source]...” if such phrasing was in its training data. This means your brand might be discussed without an explicit link, making being the known source of a fact or definition a way to get indirect credit in ChatGPT’s answers.
• ChatGPT with Browsing / Bing Chat (and similarly Perplexity) will cite sources as part of the answer. These systems tend to bracket a statement with a footnote linking to the source. The position of a citation in the answer can influence user attention. For instance, a source cited for a prominent fact in the first sentence gains more visibility than one cited later. The GEO paper introduced a Position-Adjusted Word Count metric to account for this, essentially giving higher weight to words (and citations) that appear earlier in the answer. Optimizing content to be selected for the first part of an answer (by providing a direct answer or a key fact) can maximize your brand’s exposure. The takeaway: the aim is not just to be cited, but to be cited in a meaningful, front-and-center way.
• Google SGE (AI Overviews) typically lists 3 sources (with hyperlinks) at the end of the overview, rather than inline footnotes. So if multiple sources contributed, a user might not know which part came from which source unless they click. This makes it crucial to be one of those few listed sources. As noted, SGE often draws on multiple pages, but it heavily favors content-rich subpages. It’s been observed that every page on your site, not just the homepage, can be a potential landing page for AI-driven traffic (82% of Google AI Overviews citations come from deep pages: Report). In SGE’s case, ensuring comprehensiveness and crawlability of all your informative pages is key (so that any “hidden gem” page on your site can be discovered and cited by the AI).
• Claude and other API LLMs without direct web access rely entirely on training data (unless a developer feeds it documents). They won’t cite external links in answers. For Claude, the strategy is similar to ChatGPT’s base model: if you want it to mention your brand when users ask about, say, “best X solutions”, your brand needs to have appeared in credible context in its training data (e.g. mentioned in an industry “Top X” article pre-2023). Otherwise, Claude might omit it or say it’s not familiar. Anthropic (Claude’s creator) has hinted that it trains on “publicly available internet data” similar to others, so the same bias toward high-quality sources likely applies.

In summary, LLM applications choose and reference content by first finding relevant, authoritative pages and then selecting distinctive, well-presented information from those pages to compose answers. Therefore, to maximize your brand’s visibility, you need to win at both stages: SEO visibility to get retrieved, and content optimization (GEO) to get selected and cited. In the next section, we break down concrete steps to achieve that.

Strategies to Increase Visibility in LLM Responses (GEO Tactics)