How Large Language Models Work (What Marketers Should Know)

Large Language Models (LLMs) like OpenAI’s GPT-4, Google’s Gemini, Anthropic’s Claude, Meta’s Llama 2, and the new xAI Grok are transforming how information is found and delivered. Unlike traditional search engines that index and retrieve web pages, LLM-powered tools generate answers by predicting text based on patterns learned from vast training data. This fundamental difference has big implications for digital marketing and SEO strategy. In this chapter, we’ll explore how LLMs work – from their training and knowledge limitations to issues like hallucination, memory, and relevance – and what marketers need to know to optimize content in this new era of Generative Engine Optimization (GEO).

Training Data and Language Prediction

Modern LLMs are built on transformer neural network architectures and are trained on enormous datasets of text (web pages, books, articles, forums, and more). Models such as GPT-4, Google’s Gemini, and Meta’s Llama owe their capabilities to ingesting hundreds of billions of words from diverse sources ( ^[1] ). During training, the model learns to predict the next word in a sentence, over and over, across countless examples. In essence, an LLM develops a statistical understanding of language: it doesn’t search for answers in a database at query time, but rather synthesizes a likely answer word-by-word based on the patterns it absorbed during training. For marketers, this distinction is crucial. It means that unlike a search engine that might surface your exact webpage if it’s deemed relevant, an LLM might generate an answer using information from your content (perhaps paraphrased or summarized) without directly quoting or linking to it. The model focuses on producing a fluent, relevant response – not on attributing sources by default. 

Training on vast text corpora – OpenAI’s GPT-4, for example, was pre-trained on “publicly available internet data” and additional licensed datasets, with content spanning everything from correct and incorrect solutions to math problems to a wide variety of ideologies and writing styles. This broad training helps the model answer questions on almost any topic. Google’s Gemini, similarly, has been described as a multimodal, highly general model drawing on extensive text (and even images and code) in its training. Meta’s Llama 2 was trained on data like Common Crawl (a massive open web archive), Wikipedia, and public domain books. In practical terms, these models have read a large portion of the internet. They don’t have a simple index of facts, but rather a complex probabilistic model of language.

One implication is that exact keywords matter less in LLM responses than overall content quality and clarity. Since an LLM generates text by “predicting” a reasonable answer, it might not use the exact phrasing from any single source. This means your content could influence an AI-generated answer even if you’re not explicitly quoted, provided your material was part of the model’s training data or available to its retrieval mechanisms. It also means an LLM can produce answers that blend knowledge from multiple sources. For example, ChatGPT might answer a question about a product by combining a definition from one site, a user review from another, and its own phrasing – all without explicitly telling the user where each piece came from. As a marketer, you can’t assume that just because you rank #1 for a keyword, an LLM will present your content verbatim to users. Instead, the model might absorb your insights into a broader answer. This elevates the importance of writing content that is clear, factually correct, and semantically rich, because LLMs “care” about coherence and usefulness more than specific keyword frequency. 

LLMs synthesize rather than retrieve. Traditional search engines retrieve exact documents and then rank them. LLMs like GPT-4 generate a new answer on the fly. They use their training to predict what a helpful answer sounds like. As an analogy, think of an LLM as a knowledgeable editor or author drafting a new article based on everything they’ve read, rather than a librarian handing you an existing book. This is why LLM answers can sometimes feel more direct or conversational – the model is essentially writing the answer for you. It’s also why errors (hallucinations) can creep in, which we’ll discuss later. From an SEO perspective, this generative approach means that high-quality, well-explained content stands a better chance of being reflected in AI-generated answers than thin content geared solely to rank on Google.

The model might not use your exact words, but if your page provides a clear, authoritative explanation, the essence of that information may inform the AI’s response. Conversely, stuffing pages with repetitive keywords or SEO gimmicks is less effective, because the LLM isn’t indexing pages by keyword; it’s absorbing content for meaning and then later recalling the meaning more than the literal words. It’s worth noting that LLMs are extremely large – GPT-4 is estimated to have over 1.7 trillion parameters (the internal weights that store learned patterns). These parameters encode probabilities for word sequences. When an LLM answers a query, it starts with the user’s prompt, and then it internally predicts a sequence of words that statistically and contextually fit.

The self-attention mechanisms in transformers allow the model to consider relationships between words and concepts even if they are far apart in the text. For example, if a user asks “How does a hybrid car work?”, the model doesn’t search for a specific document. Instead, it uses its trained neural connections (built from seeing millions of words about cars) to produce an explanation, perhaps describing the battery, electric motor, and gasoline engine in seamless prose. It might have “seen” text about hybrid cars during training, but it’s not copying one source – it’s generating new sentences that sound like what it learned. This ability to synthesize means content creators should focus on providing comprehensive coverage of topics in a way an AI can easily learn from. In other words, ensure your content teaches well – because the better the AI can learn your information, the more likely it is to use it when generating answers. 

Why this matters for marketers: 

In the old SEO paradigm, one might obsess over exact-match keywords or getting a featured snippet. In the new GEO paradigm (Generative Engine Optimization), the emphasis shifts to being part of the model’s knowledge base and providing information in a format the model can easily understand and repurpose. If your content is high-quality and clearly written, an LLM is more likely to have that knowledge and use it. If your content is thin, misleading, or overly optimized just for search crawlers, an LLM may either ignore it or, worse, learn incorrect information from it (which could later reflect poorly in AI outputs). The bottom line is that LLMs reward clarity and depth. As one SEO expert put it, structured writing and genuine semantic clarity are not optional in the age of generative AI – they are essential. LLMs don’t look for a <meta> tag to figure out your page’s topic; they literally read your content like a very fast, very well-read user. Thus, good writing and organization become your new SEO superpowers.

Knowledge Cutoffs vs. Real-Time Data: Retrieval-Augmented Generation (RAG)

One limitation of many LLMs is the knowledge cutoff – the point in time after which the model has seen no new training data. For instance, the base GPT-4 model (as of early 2024) has a knowledge cutoff around September 2021. This means if you ask it about an event or statistic from 2022 or 2023, it might not know about it from its training alone. Similarly, Meta’s Llama 2 has a training cutoff of late 2022. This poses a challenge: users expect up-to-date information, but these models’ “memory” can be frozen in time. To address this, AI developers have introduced real-time retrieval mechanisms that supplement the static knowledge of LLMs with fresh information. This approach is broadly known as Retrieval-Augmented Generation (RAG).

In a RAG system, when the user asks a question, the AI first performs a search or lookup in an external data source (for example, a web search index, a company knowledge base, or a database of documents) and retrieves relevant text. That retrieved text is then fed into the LLM along with the user’s query, giving the model “grounding” facts to base its answer on ( ^[2] ). The LLM then generates a response that incorporates that up-to-date information. Essentially, RAG combines the strengths of search (accurate, current data retrieval) with the strengths of LLMs (fluent natural language answers). 

Examples of RAG in action: 

• If you use Bing Chat, you’re seeing RAG at work – Bing’s AI (which uses GPT-4 under the hood) will actually search the live web for your query, then use the web results to formulate an answer, often citing sources.
• Another example is Perplexity.ai, an AI search engine that always provides citations: when you ask Perplexity a question, it finds relevant up-to-date sources (e.g. news articles, websites) and then generates a concise answer with footnotes linking to those sources.
• Google’s Search Generative Experience (SGE), currently in preview, also uses live search results to generate an “AI overview” at the top of the page for certain queries. This overview is built by the LLM reading top search hits and synthesizing them.

In all these cases, the AI is not limited to its stale training data – it can pull in new information on the fly. An example of real-time AI retrieval: Perplexity.ai answers a query about the latest Madonna concert setlist by fetching current info (March 2024) and citing trusted sources. This Retrieval-Augmented Generation approach ensures up-to-date, factual answers, addressing the knowledge cutoff problem. 

For marketers, RAG is a double-edged sword.

On one hand, it means fresh content can be surfaced by AI even if that content wasn’t in the model’s original training. If you publish a blog post tomorrow and it starts ranking or is deemed relevant, an AI like Bing or Perplexity might pull it in to answer user questions next week. This is encouraging – it preserves some role for traditional SEO (you still want to appear in those top results that the AI will consider).

On the other hand, if AI platforms are giving users the answers directly, the click-through to your site may be reduced. We’ll discuss metrics in a later post, but it’s important to recognize that RAG-driven answers often satisfy the user without a click (especially if the answer is fully contained with citations). Your content’s value might be realized by informing the AI’s answer rather than driving traffic. This makes brand visibility (being mentioned or cited by the AI) a new key goal alongside traditional clicks.

Marketers should also understand how RAG selects information. Typically, a retrieval algorithm (which might be a traditional keyword-based search or a vector similarity search) finds text passages that likely answer the user’s query. Those passages are then given to the LLM. Importantly, even sophisticated AI search still often relies on keyword matching for the retrieval step. In other words, to be one of the sources an AI pulls in, your content likely needs to contain the keywords or phrases the user’s query uses (or very close synonyms). The LLM itself can understand nuanced content, but if the retrieval mechanism doesn’t surface your page, the model won’t even see your content. As one analysis noted, the “retrieval layer” that decides what content is eligible to be summarized is still driven by surface-level language cues – in experiments, simple keyword-based retrieval (BM25) outperformed purely semantic approaches for feeding documents to the LLM.

In plain terms: even for AI-generated answers, classic keyword strategy isn’t dead. Clear, literal wording that matches user queries helps ensure your content is selected as input to the AI. So while LLMs themselves don’t need exact-match keywords to understand text, the pipeline bringing them content does often depend on those keywords ( ^[3] ). A user prompt like “Show me articles about LLMs using schema” will cause the system to fetch content that explicitly mentions “LLMs” and “schema,” not just content that implies those concepts ( ^[3] ). This means you should still align your content with the language your audience uses in queries. Another aspect of staying visible in the age of RAG is ensuring your content is accessible to AI crawlers and indexes.

For example, OpenAI introduced GPTBot, a web crawler that browses the internet to collect content for future model training. GPTBot honors robots.txt ; by default it will crawl sites to gather data that could be used in the next GPT model. Some website owners have chosen to block GPTBot due to privacy or IP concerns. As of mid-2025, over 3% of websites globally (and a larger share of top sites) disallow GPTBot. This is an important strategic decision. 

If you block AI crawlers from training on your content, your information might not be present in the next generation of models. That could limit your brand’s visibility in AI answers. It’s a trade-off: some publishers worry about giving content away to AI without direct compensation or traffic, while others see being included in AI training sets as a way to ensure their brand knowledge is widespread. As noted in an industry discussion, blocking GPTBot “restricts your content from being used in AI-generated responses, which can limit brand visibility in tools that now dominate early-stage discovery.” Conversely, allowing it means “your brand [can] show up in ChatGPT answers,” potentially reaching a massive user base.

In fact, ChatGPT reportedly reached around 800 million weekly users at one point – a staggering audience you’d probably want your content to be exposed to. 

Making content accessible goes beyond GPTBot. It also means continuing to allow indexing by search engines (since AI search experiences like SGE or Bing are built on top of search indexes) and ensuring your content isn’t buried behind logins or paywalls (unless your business model demands it). If you have a developer API or data feed, you might even consider making certain data available to trusted AI partners – for instance, some e-commerce sites might feed product info to Bing’s index so that Bing Chat can answer product questions with live data. The key point is that content availability equals AI visibility. Marketers should keep an eye on emerging standards for AI inclusion/exclusion (similar to robots.txt but for AI).

For now, the pragmatic approach to GEO is to welcome reputable AI crawlers : this can help AI models stay up-to-date on your offerings and reduce the risk of misinformation (since the AI will have your latest, correct info to learn from). It’s also worth mentioning that some AI platforms (OpenAI, Meta, etc.) do periodically retrain or fine-tune their models with newer data. OpenAI has hinted at GPT-4 updates that include some post-2021 knowledge via fine-tuning or the plugin/browsing features. Google’s Gemini is likely trained on more recent data (possibly through 2023) and Google can continuously infuse freshness via its search index. Anthropic’s Claude regularly gets fine-tuned with more recent content as well.

In the enterprise space, companies are deploying internal RAG systems that connect LLMs to their up-to-the-minute databases. All this means the gap between what happened today and what the AI knows is closing, but not entirely gone. Marketers should still produce timely content (AI will find ways to use it via retrieval) and also evergreen content (to be included in base training sets and long-term AI knowledge). Ensure that when the next wave of model training happens, your site is crawlable and your content is high-quality – that maximizes the chance that the AI will “learn” your content.

OpenAI’s own GPT-4 documentation notes that its training data included a diverse mix intended to capture “recent events” and “strong and weak reasoning” etc., but it admitted a knowledge cutoff in 2021. With GPT-5 or others, those cutoffs will extend. The concept of GEO includes being prepared for your content to be training data. For instance, if your site offers an FAQ or glossary in your niche, that’s exactly the kind of text likely to be scooped up and learned by LLMs (because it’s explanatory and authoritative).

In a sense, content SEO and “training SEO” become one : you write for users and for the AIs that read over the users’ shoulders. In summary, RAG and real-time data integration are bridging the gap between static AI knowledge and the current world. Marketers must adapt by (a) keeping content accessible and indexable to these systems, (b) continuing to optimize for relevant keywords and clear language so that retrieval algorithms can find you, and (c) recognizing that being the source of truth in AI answers (even if indirectly) is the new win. A practical tip: monitor where your content might be appearing in AI citations. For example, if Perplexity or Bing Chat often cites your blog, that’s a good sign your GEO strategy is working.

Some SEO tools now even track “AI mentions” or how often an AI assistant references a brand. We’ll cover measurement in a different post. Before moving on, it’s important to note that RAG isn’t just about freshness – it’s also a solution for accuracy, which leads us into the next topic. By grounding answers in real sources, RAG significantly reduces the incidence of AI hallucination and increases trust (users can see citations). Let’s discuss hallucinations and why factual accuracy is a critical concern.

Hallucinations and Accuracy Challenges

One of the most notorious quirks of LLMs is their tendency to “hallucinate” – in other words, to produce information that sounds confident and specific, but is completely made up or incorrect. Unlike a search engine that simply might not return a result if it doesn’t have one, an LLM will always try to answer your question. If the model doesn’t actually know something, it will improvise, drawing on its training patterns to create a plausible-sounding answer. This can range from minor inaccuracies (getting a date or name wrong) to completely fabricated facts, citations, or even quotes. For individuals and brands, hallucinations aren’t just academic errors – they can be reputational or legal landmines.

Consider a real-world example: in April 2023, an Australian mayor discovered that ChatGPT was mistakenly stating he had been involved in a bribery scandal and even served prison time – none of which was true. The mayor was actually the whistleblower who exposed that scandal, not a perpetrator. He was understandably alarmed and pursued what could be the first defamation lawsuit against OpenAI for this false claim. ChatGPT had essentially hallucinated a criminal history for a real person, potentially damaging his reputation. The mayor’s lawyers noted how the AI’s answer gave a false sense of authority – because ChatGPT does not cite sources by default, an average user might just assume the information is correct. This case illustrates the brand risk: an AI could incorrectly describe your company or a public figure associated with you, and users might believe it because of the AI’s confident tone.

Another well-known incident: In mid-2023, a pair of New York lawyers filed a legal brief that cited six precedent court cases – all of which were fake, invented by ChatGPT. The lawyers had used ChatGPT to research cases, and the AI provided entirely fictitious case names and summaries that sounded authentic (complete with docket numbers and judges’ names). The judge was not amused; he sanctioned the attorneys with a fine and reprimand. The lawyers admitted they never imagined the AI would just make up cases “out of whole cloth”. This example underscores that even highly educated professionals can be misled by AI hallucinations if they aren’t careful. It also highlights a crucial point: LLMs do not have an internal database of verified facts – their knowledge is statistical, not deterministic. Without external verification, they might output incorrect information that looks perfectly credible.

From a marketing perspective, hallucinations pose a risk to brands and a challenge to AI adoption. If an AI chatbot erroneously states something about your product (say, it hallucinates a feature that doesn’t exist or confuses your product with a competitor’s), customers could be misinformed. Or the AI might misquote a statistic from your content, undermining your thought leadership with inaccuracies. Even more concerning, if the AI has absorbed biased or untrue statements about your brand from somewhere, it might repeat them. Brands have already started monitoring AI outputs for such mentions.

For example, if you’re a PR manager, you might now need to check not just Google results for your brand, but also ask ChatGPT or Bing Chat “What does [Brand] do and is it reliable?” to see if the AI says anything incorrect or damaging. Why do hallucinations happen? At a technical level, it’s because LLMs are optimized to produce fluent language that seems right, rather than to internally fact-check against a knowledge graph. The model is driven by probabilities – it knows what words often co-occur. If asked a question it’s unsure about, it will generate something that usually would be an answer. It has no intrinsic concept of truth, only what it learned during training. If the training data was sparse or conflicting on that point, or if the query is very specific, the model may just pick a likely-sounding completion.

For instance, early versions of Google’s Bard made a notable mistake when asked about the James Webb Space Telescope – Bard confidently gave an incorrect fact, which in a high-profile demo led to criticism and even a dip in Google’s stock price. The model wasn’t trying to lie; it just didn’t know the correct answer and guessed. Some metrics show the scale of the issue: in a study examining LLM responses in a scientific context, GPT-3.5 was found to produce a hallucinated reference (a made-up citation) about 40% of the time, and even GPT-4 did so about 28% of the time. Google’s Bard, in that 2023 study, hallucinated references a whopping 91% of the time.

While those numbers may vary by context and have likely improved with model updates (and Bard has since been upgraded, possibly via Gemini), the takeaway is that even the best models currently in use are far from perfectly accurate. They do make things up. GPT-4 is more reliable than its predecessors (OpenAI claims it reduces hallucinations significantly, and indeed GPT-4’s hallucination rate is lower than GPT-3.5’s), and newer iterations (like a hypothetical GPT-4.5 or GPT-5) are expected to further improve. Anthropic’s Claude has been designed with constitutional AI principles to avoid incorrect statements, and users often report it has a slightly different style that can reduce certain errors.

But no LLM is 100% factual. Marketers must therefore approach AI content generation with a critical eye: AI can accelerate content creation and user interaction, but its outputs must be verified, especially on factual details. What can be done about hallucinations?

There are a few approaches, and many tie back to content strategy: 

1. Authoritative, well-structured content: If your website clearly and unambiguously states facts about your domain, an LLM is less likely to hallucinate when using your content. Conversely, if correct information is scarce or drowned in a sea of speculation online, the model may latch onto the wrong patterns. This is why one recommendation is to publish fact sheets, Q&As, and data pages about your brand or industry. By seeding the web (and thus training data) with accurate information, you help steer the AI’s model. It’s akin to SEO in the sense of providing the canonical answer for your area of expertise. 
2. Retrieval and citations (RAG): As covered above, retrieval-augmented generation can drastically cut down hallucinations. When the model is forced to consult external sources (like a live database or a snippet from your site) before answering, it is more likely to stay factual ( ^[2] ). That’s why tools like Bing Chat or Perplexity that cite sources tend to inspire more confidence – if the AI says “According to [Source], the product weighs 1.2 kg,” and gives you the source, you trust it more and the chance of a total fabrication is lower. Marketers integrating AI into user experiences (e.g., a website chatbot) should strongly consider a RAG approach: have the bot pull answers from your knowledge base or site content, rather than relying purely on its pre-trained memory. Not only does this improve accuracy, it also allows you to update information immediately (update the knowledge base and your AI assistant will reflect that, without needing a full model retrain). 
3. Human oversight and fact-checking: When using generative AI for content creation, implement a review process. If AI writes a draft of an article, have a subject matter expert review every claim and statistic. AI can save you time by generating well-structured text, but you must ensure it hasn’t introduced a false “fact.” For instance, if you prompt ChatGPT to write “10 Benefits of Product X,” it might invent a benefit that isn’t real if it runs out of sourced material. It’s up to you to catch that. Treat AI outputs as you would a human junior copywriter’s work – useful, but requiring editorial oversight. 
4. Model improvements: The AI research community is aware of hallucination issues and is actively working on them. Techniques like reinforcement learning from human feedback (RLHF) have been used to fine-tune models to be more truthful. OpenAI, for example, had humans vote on preferred answers which presumably included preferring correct over incorrect answers, thereby nudging GPT-4 to lie less. Other approaches involve adding modules for verification – e.g., after the model generates an answer, have it check the answer against a trusted source (sort of a self-RAG). While these innovations are promising, from a marketer’s standpoint it’s safer to assume the AI will make mistakes and plan accordingly, rather than waiting for a “perfectly honest” model.

The implications for brands are also prompting conversations about governance and liability. If an AI platform repeatedly hallucinates harmful falsehoods about businesses or people, will there be legal repercussions? The Australian mayor’s threatened lawsuit is a test case. In another case in the US, a radio host sued OpenAI after ChatGPT falsely accused him of embezzling money – that case was dismissed on the grounds that OpenAI itself didn’t publish the info (someone’s usage of ChatGPT did), but we’re in uncharted territory legally. Marketers should thus monitor AI outputs for their brands similarly to how they monitor social media or press mentions. We might see the rise of “AI Reputation Management” as a field. On the flip side, there are opportunities: ensuring your brand has a strong, positive presence in the data that AIs train on (through content marketing, PR, etc.) could help the AI tell your story correctly. For example, if you publish an open dataset or detailed history about your company, a future LLM might learn from it and convey that information to users accurately, rather than pulling from a dubious blog post written by someone else.

In summary, hallucinations are a current reality of LLMs – they can and do fabricate information. This elevates the importance of authoritative content and fact-checking. Brands should double down on being the source of truth in their domain. By doing so, you reduce the chance that an AI fills a knowledge gap with nonsense. And when using AI outputs, maintain a healthy skepticism. As one AWS expert quipped, an LLM on its own can be like “an over-enthusiastic new employee who refuses to stay informed with current events but will always answer every question with absolute confidence.” You wouldn’t let a new hire present to clients unsupervised on day one – likewise, let’s not let AI outputs go live without a sanity check. The goal is to harness LLMs’ productivity and conversational power while safeguarding accuracy – a balance that is still being learned industry-wide.

Context and Memory: Multi-Turn Conversations

A major evolution from traditional search to LLM-based chat interfaces is the concept of contextual, multi-turn conversation. In a normal search engine, each query you type is independent – the search engine doesn’t remember what you asked 5 minutes ago. In contrast, when you interact with an AI chatbot (be it ChatGPT, Bing Chat, Google’s Bard/Gemini, or others), the system retains memory of the dialogue (up to certain limits) and uses that context to inform subsequent responses.

This fundamentally changes how users seek information and how content might be consumed or referenced over multiple turns. LLMs remember the conversation (up to a point). Technically, this is handled via the “context window” of the model – a rolling buffer of the last N tokens of dialogue that the model takes into account. Early versions like GPT-3 had context windows of around 4,000 tokens (roughly 3,000 words). Newer models have expanded this greatly: OpenAI’s GPT-4 offers variants with up to 32,000 tokens (24k words) of memory, and Anthropic’s Claude went even further with a staggering 100,000-token context window (approximately 75,000 words).

To put that in perspective, Claude can ingest an entire novel or a lengthy research report in one go and discuss it. Claude’s creators demonstrated this by feeding it the full text of The Great Gatsby (72K tokens) and asking a detailed question about a subtle change – Claude answered correctly in seconds. This long memory enables conversations that can reference a large document or many prior messages without losing track. For marketers, the immediate implication is that AI chatbots can handle complex, in-depth queries that build on each other.

A user might start general (“What are the benefits of electric cars?”), then follow up with something more specific (“How does the maintenance cost compare to hybrid cars?”), and then maybe, “You mentioned battery lifespan, can you provide more details on that for a Nissan Leaf?” In a chat scenario, the AI will carry forward all relevant information from earlier in the chat when answering the later questions. It behaves more like a human advisor who remembers what you already asked and what they already told you. So how do we optimize content knowing that user interactions might be multi-turn and contextually layered?

Here are a few considerations:

Chunk your content into logical sections that can stand alone. Since the AI may not present an entire webpage to the user but rather use pieces of it across different turns, it helps if each section of your content addresses a specific subtopic clearly. For instance, if you have a product FAQ page, ensure each Q&A pair is self-contained. If the chatbot draws on a particular Q&A to answer one question, and then the user asks a follow-up, the AI might go back to the same source or related ones. If your content is written in long, interwoven paragraphs that mix many ideas, the AI might extract an incomplete snippet that doesn’t carry the full context to the next turn. Conversely, if each paragraph or section covers one idea succinctly, the AI can quote or summarize that section when needed without misrepresenting it.

Use consistent terminology and references. In a conversation, pronouns and references matter. For example, if your content refers to “Product X” in one paragraph and then “the device” in the next, a human understands those are the same, but an LLM might need clear signals to maintain reference across turns. In a multi-turn dialogue, the AI uses its memory of previous mentions. If a user asks about “the device” later, the AI will try to link that to “Product X” mentioned earlier, but clarity helps. Make sure your content uses names and terms clearly so that if an AI mentions “Product X” in one answer, it can easily continue talking about it in follow-ups without confusion. A good practice is to include brief re-introductions of key entities when transitioning topics (much as a well-written article might do). This mirrors what the AI will do – it often rephrases or reintroduces context for itself as the conversation goes on.

Provide summary and recap sections. Because an AI will keep earlier context in mind, if your content includes a short summary or highlights, it might preferentially use that summary when the user drills down. For instance, imagine a user asks “Tell me about company ABC.” The AI might pull a summary from ABC’s “About Us” page. If the next question is “What were their revenues last year?” – the AI might recall a specific figure from earlier content or it might quickly scan for a number in its stored context. If your content had a quick facts section (“Founded: 2010, Revenue 2024: $50M, Employees: 200”), the AI can answer directly with that data. If not, it might generate an approximation or skip it. Essentially, having structured data or concise facts in your content helps the AI retrieve those facts in multi-turn conversations. (Structured data markup can help as well – we’ll cover that in a different article – and indeed Google’s AI overview has been said to leverage structured data where available.)

Conversational tone and FAQ format can be beneficial. Content that is already in a conversational Q&A style is naturally aligned with how users interact with chatbots. Many businesses are now adding FAQ sections or conversational snippets to pages (e.g., “Q: What does this product do? A: It helps you…”) not just for traditional SEO (featured snippets) but for AI. If a user asks an AI “What does [Your Product] do?”, the AI might directly use the Q&A from your site if it’s clearly written, rather than synthesizing an answer from scratch. Moreover, in a multi-turn exchange, if the AI gave a general answer initially, and the user asks a more specific question, the AI might look for a specific Q from an FAQ that matches. Embedding likely user questions into your content (and answering them) is a strategy (often called answer engine optimization (AEO) in the SEO community) that overlaps heavily with GEO. You want to be the source of those bite-sized answers the AI delivers.

Let’s illustrate how multi-turn memory can play out with a hypothetical scenario: A user is planning a vacation and interacting with a travel AI assistant. The user says, “I’m interested in visiting Greece in September.” The AI might give an overview of Greece in September, perhaps noting weather, events, etc., citing sources like travel blogs or Greek tourism sites. Next, the user asks, “What are some must-see historical sites there?” – because the AI remembers we’re talking about Greece, it doesn’t need the user to repeat “in Greece”. It will list sites like the Acropolis, Delphi, etc., maybe quoting a site about Greek historical attractions. Then the user says, “How about on the islands? I’m thinking Crete or Rhodes.” Now the AI needs to recall that we are still on the topic of historical sites and Greece, and specifically islands.

It might then give an answer about the Palace of Knossos in Crete and the Colossus site on Rhodes, for example, pulling from those specific island tourism guides. In doing so, the AI might have retrieved information from different pages for each turn, but it keeps the conversation flow. From an optimization standpoint, if you are a travel marketer wanting your content in that mix, you’d want to have pages like “Top Historical Sites in Greece,” and maybe separate ones like “Top 10 Things to Do in Crete” that mention Knossos. The AI could use the first page for the mainland answer and the second for the island follow-up. If your Crete page has a section titled “Historic Attractions in Crete” with Knossos clearly described, the AI can more readily pull that for the user’s follow-up question about islands. On the other hand, if your info is scattered or under generic titles like “All About Crete” (where history is buried under beaches and food info), the AI might miss it or not find it quickly enough in context.

Another aspect of memory is persistent user preferences or data. Some advanced AI systems (and likely future personal AI assistants) could remember user-specific info across sessions (with permission). For instance, a user might tell an AI “I have gluten allergy” in one turn, and later on, while asking about recipes or restaurant recommendations, the AI will keep that in mind and filter answers.

As a marketer, consider how your content might be parsed in light of such personalized context. If you run a restaurant and have a menu page, clearly labeling which items are gluten-free or vegan (with text the AI can read, not just icons) will be important so that an AI assistant can say “Yes, this restaurant has 5 gluten-free entrees” in a conversation. Essentially, clarity in content helps AI not just generally but in delivering personalized answers matching the user’s context. It’s also useful to understand the limits of AI memory. While LLMs can carry a lot of context, they do have finite windows.

For example, ChatGPT with GPT-4 8K can handle roughly what’s in a few pages of text; with 32K, maybe a small ebook’s worth. Claude with 100K can handle huge texts, but even that has limits (about 75k words as noted). If a conversation goes on and on, older parts of the dialogue might get dropped or summarized to stay within limits. So, if a user has a very lengthy interaction (say, 100+ turns), the AI might not perfectly recall details from the very beginning unless it was explicitly reinforced or repeated. That’s one reason why reinforcing key points in your content is helpful – if an AI saw it multiple times or in summary form, it’s more likely to stick in the conversation. The Anthropic example showed that the AI could find a very specific changed line in Gatsby because it had the whole text in context. But not every AI will load an entire page; many times they only use a snippet that looked relevant. If further questions require more detail from that page, the AI might go back and fetch more.

Ensuring your page is easily navigable (clear subheadings, jump links, etc., which an AI can use similarly to how a human would) could facilitate the AI retrieving the needed context again. Multi-turn SEO (or GEO) is still an emerging idea, but it boils down to this: Think about conversational workflows. In the past, you might have thought of search queries in isolation (“user searches X, lands on my page, leaves or converts”). Now, think in terms of dialogue: “User asks broad Q (AI gives overview, maybe mentions my brand), user asks specific Q (AI pulls a specific fact, maybe from my site), user asks comparative Q (AI might use data from me and competitor side by side), user then decides next step.” In that chain, you want your information to be present and accurate at each relevant step.

This could mean having a mix of content: broad explainers for the top-of-funnel overview answers, detailed specs or data for the mid-funnel detailed questions, and perhaps even user-generated content or reviews (if you host those) for questions about experiences or opinions (which AI might surface e.g. “what do people say about Product X’s battery life?”). One more point: context extends to user context, not just conversation context. LLM-powered systems could use contextual signals like location, time, or user profile to tweak answers. For instance, if someone asks an AI voice assistant “What should I have for dinner?” the AI might consider it knows the user is vegan and it’s 5 PM on a weekday – it might answer differently for that user than for another. While this strays into personalization, it’s related to context memory because the AI could remember preferences. Marketers should be mindful of providing content that can feed into these contextual pivots.

If you have schema markup for your restaurant that indicates “vegan options available” or if your recipe site has tags for “quick weeknight recipe,” those pieces of data could influence whether the AI picks your content when the user’s context is known (e.g., weeknight + vegan filter). In short, structuring content for various contexts (dietary, seasonal, regional, etc.) can pay off in a world of personalized AI answers. To sum up, LLM memory transforms search into a conversation. This rewards content that is structured, clear, and modular enough to be used in a piecemeal yet coherent way. It also opens opportunities for guiding users down a journey through content via the AI. Each turn is like a query, and your content should ideally be the answer to one of those queries. When optimizing now, ask yourself: If I were a chat AI, what follow-up questions might a user ask after this, and do I have content that answers those? This aligns with strategies like intent mapping and content clustering that SEO specialists already use (anticipating user follow-up questions and covering them). Now, those follow-ups might happen with the AI as the intermediary, but the logic remains: comprehensive, well-organized content wins.

Ranking vs. Relevance in LLM Outputs

In the era of classic search engines, success was largely about ranking – could you get your page to rank #1 for a target query, or at least on the coveted first page of results? The battle for the top spot drove the entire SEO industry. In the emerging era of AI-generated answers, the game shifts to relevance and representation – ensuring your content is chosen by the AI as part of its answer, even if your site itself isn’t shown as a traditional “blue link.” This doesn’t mean traditional ranking factors are irrelevant (Google and Bing still have their algorithms feeding into the AI), but the paradigm of how content is delivered to users is changing. 

AI search is about answers, not links. As a Google article succinctly put it, in generative AI search, the system isn’t retrieving a whole page and showing it; it’s building a new answer based on what it understands. The AI might pull one sentence from your site, another from someone else’s, and then stitch them together in a coherent paragraph (often paraphrasing along the way). For the user, this is convenient – they ask a question and get an immediate, consolidated answer. But for content creators, it raises the question: How do I get the AI to “pick” my content as part of that answer? We can think of this as the content being ranked internally by the AI for relevance, even if no explicit ranking is shown to the user.

One key is semantic clarity and structured information. LLMs interpret web content differently from search engine crawlers. They ingest the full text and analyze the relationships and meanings, rather than just looking at meta tags or link popularity. They pay attention to things like the order of information, headings and subheadings that denote hierarchy, and formatting cues (bullet points, tables, bold highlights) that signal important points. If your content is well-structured and clearly written, the LLM can understand it better, which increases the chance it will use it in an answer. Think of headings as signposts for the AI – a clear H2 like “Benefits of Solar Panels for Homeowners” tells the model that the following text likely contains a direct answer if a user asks “What are the benefits of solar panels for a homeowner?”. If instead your page has a clever or vague heading (e.g. “Shining Bright!” for that section), the AI has to infer what that section is about. It might still figure it out from the text, but you’ve added friction. As Carolyn Shelby writes in Search Engine Journal, poorly structured content – even if keyword-rich and marked up with schema – can fail to show up in AI summaries, while a clear, well-formatted piece without a single line of schema can get cited or paraphrased directly.

In other words, content architecture beats metadata hacks in the AI world. That’s not to say schema isn’t useful (it can help, and Google has confirmed their LLMs do take structured data into account), but if you had to prioritize: make the core content extremely clear and skimmable. An AI is essentially a super-fast reader – it should be able to glance through your content and quickly grasp the key points. If your page has one H1 and 20 H2s all named something quirky, it might “confuse” the model’s understanding of what’s important. Logical nesting of H1 > H2 > H3 (with meaningful titles) essentially provides an outline to any reader, human or AI. Use that to your advantage. 

Direct answers and snippets. Just as featured snippets in Google were often drawn from concise answer boxes in content (like a definition in a single sentence, or a numbered list of steps), AI answers often prefer content that is formatted for easy extraction. Lists, steps, tables, and FAQs are golden ( ^[4] ). For example, if the query is “steps to change a tire,” an AI will very likely present a step-by-step answer. If your article “How to Change a Tire” has a nice ordered list of steps 1 through 5, there’s a good chance the AI might use your list (perhaps reworded) in its answer, possibly even citing you. If your article instead is a long narrative with the steps buried in paragraphs, the AI might still glean the steps but could just as easily use another site’s list instead. We see this with tools like Bing Chat – it often pulls bulleted or numbered lists from sites to give to the user (with little [source] annotations). Perplexity, which always cites sources, tends to pull concise statements. If someone asks a medical question, Perplexity might grab a one-line definition from Mayo Clinic or WebMD rather than a verbose explanation from elsewhere, because it’s easier to drop that one line into an answer.

So, make your key points concise and standalone. This doesn’t mean oversimplify everything; it means consider adding summary sentences or bullet points that encapsulate the detailed text. A good practice is to front-load key insights in your content. Don’t bury the lede. LLMs, like rushed readers, often prioritize what comes first in a section or document. If the first sentence of your intro is a crisp summary of the answer, the AI might use that and move on, whereas if you only reveal the answer in the conclusion, the AI might have already compiled an answer from other sources by then. Another concept emerging is the “AI citation economy” ( ^[5] ). When AI summaries (like Google’s SGE or Bing) do cite sources, being one of those sources can drive some traffic and certainly visibility. There’s anecdotal evidence that being cited in SGE can result in clicks if users want to “learn more” and trust the snippet they saw. Bing’s citations [numbers] are clickable and some users do click them.

So how to get cited? Based on observation and some studies (like the BrightEdge analysis of SGE vs Perplexity citations), authoritative domains have an edge, and content format matters. Authoritative doesn’t just mean high Domain Authority; it can also mean niche authority. For instance, a well-structured blog post from a lesser-known site can still get cited if it directly answers the question better than a higher-authority site. That said, sites like Wikipedia, Britannica, official government sites, etc., are heavily cited by AI because they are factual and straightforward. If you’re outranked by such sites in regular search, you’ll likely also be “out-cited” by them in AI answers. The strategy here is to find the questions where you have a unique value or perspective that the generic sources don’t, and ensure your answer is crystal-clear. For example, maybe no Wikipedia article gives a step-by-step of a specific software troubleshooting that your site does – then your steps might get picked by the AI. Or your e-commerce site might have very specific data on product dimensions that general sites don’t list, so an AI might cite you as the source for “weight: 1.2 kg” if a user specifically asks about that.

The granularity and uniqueness of information you provide can make you the go-to source for certain details. We should also address overlap and differences between optimizing for classic SEO vs. LLM SEO (LLMO). There’s overlap: things like clear headings, good content, authoritative backing – these were always SEO best practices and remain so. But gaps exist: for example, link-building, a cornerstone of SEO, might not directly translate to AI answer optimization. An LLM doesn’t care how many backlinks your page has when it’s generating an answer (though the retrieval algorithm that selects your page might use PageRank or similar, indirectly making backlinks still relevant). But the AI model itself isn’t making a judgment like “this site has lots of links, so its content must be good.” It’s judging content quality more intrinsically – coherence, completeness, readability.

This suggests that on-page content quality matters even more, whereas off-page signals might influence whether you get retrieved in the first place (since search indexes and authority still feed into what content the AI sees). Google’s generative search, in particular, seems to often pull from pages that were already ranking in the top results (no surprise since it’s built on Google Search). BrightEdge’s study noted a lot of overlap in which domains get cited by SGE and by Perplexity. Big hitters like Wikipedia and official sites frequently appear. But also interesting is that different AI systems have different citation patterns – Perplexity, for instance, might favor certain tech forums or Reddit for some queries, whereas SGE might stick to more formal sources. Knowing these tendencies can inform strategy: e.g., if Reddit is often cited for certain tech questions and you run a tech company, maybe it’s worth engaging in those communities or providing expert answers there (so that when AI pulls from Reddit threads, your answer could be included). That ventures into off-page tactics, but it’s an example of thinking beyond your own site. 

Semantic relevance vs. exact keywords. We touched on this with retrieval, but it’s worth reinforcing: meaning is king for LLMs. If your content semantically answers the question, the AI can use it even if wording differs. However, when it has many choices, it may lean to content that more literally matches the question (since it’s “safer”). For instance, a user asks, “How can I improve my website’s accessibility for visually impaired users?” Suppose you have an article titled “Making Websites Screen-Reader Friendly” – that’s on-topic but not a word-for-word match. Another site has an article “How to Improve Website Accessibility for Visually Impaired Users” – basically the query terms as a title. If all else is equal, the AI might pick text from the latter because it’s an obvious direct match. This echoes traditional SEO advice: align with user language. In the SEJ example, the author’s article about “AI search” didn’t show up for an LLM query about “LLMs and schema” because it never explicitly said “LLM,” even though it was relevant ( ^[6] ) ( ^[7] ). The model had plenty of other content with the exact term, so it used those.

The lesson: don’t shy away from using the same terminology your audience uses, even as you focus on depth and quality. LLMs are smart but when composing an answer, they might play it safe by quoting content that literally contains the asked-for terms ( ^[3] ).

Finally, consider user engagement signals in an AI context. Traditional Google ranking has long debated using pogo-sticking or time-on-page as signals (with mixed evidence). In an AI answer scenario, if the user is satisfied, they might not click anything at all. If they’re not, they might click one of the cited sources or ask a follow-up. It’s conceivable that if users frequently click a particular citation after seeing an AI answer, that might be a sign that the citation had more to offer or the AI answer was lacking detail from that source. AI providers could use such feedback to adjust which sources are chosen or how answers are formulated. We don’t know for sure yet, but user behavior in interacting with AI results could indirectly affect which sources get favored over time.

For marketers, this means if you do get cited, make sure the page the user lands on is high quality and answers what the AI snippet couldn’t (encourage the user to stay and explore). If the AI summary only gave a teaser and users click through to your site for full info, that’s great. But if the AI gave almost everything and the user has no reason to click, you got visibility but no visit. In such cases, think about content depth : maybe providing some unique value (interactive element, tool, community, etc.) beyond the text that the AI used. That can entice users to learn more on your site even after a good AI answer.

In conclusion, the focus shifts from ranking to being the reference the AI trusts. In GEO, your content’s structure, clarity, and authority determine if the AI will choose your snippet in its synthesized answer. Clean, well-segmented content is now a kind of AI ranking factor ( ^[5] ). Success is measured by whether the AI includes your brand or content in answers (and ideally cites it) – even if the user never sees a traditional search results page. The overlap with traditional SEO is substantial – good content is good content – but the way that content is evaluated and utilized by AI introduces new nuances. By aligning with how LLMs parse and generate information, you increase your chances of remaining visible and relevant in a future where answers, not links, are the immediate output of a search.

In this article, we explored the inner workings of LLMs from a marketer’s perspective: how they learn and generate text, how they handle new information and context, why they sometimes err, and how they incorporate content into answers. The recurring theme is that many classic SEO best practices (quality content, structured pages, understanding user intent) are not only still valid – they’re essential for GEO. At the same time, we must adapt to the nuances of AI-driven search: optimizing for an answer engine that writes summaries and engages in dialogue, rather than a static list of ranked links. As we move forward, the subsequent blog posts will build on this foundation.