Where LLMs get the real-time data behind AI search (and why it matters more than you think)

Earlier this week, we published a piece titled, “From SEO to Generative Engine Optimization (GEO): Why the New Era of Search Belongs to AI — and How to Stay Visible.” In it, we explored how SEO is slowly losing its grip on digital visibility, and how we’re entering a new era defined by AI-native discovery. The article unpacked the deeper shift in how people search, discover, and engage with content online.

For more than two decades, Google dictated the rules: type a keyword, scroll through blue links, click a few. That model built a $200+ billion empire. But today, that system feels increasingly outdated.

In 2024 and beyond, users aren’t searching — they’re prompting. Whether it’s ChatGPT, Gemini, or Perplexity, people expect instant, high-quality answers delivered in a single conversational box. They don’t want ten blog posts. They want the best answer — now.

“In the age of ChatGPT, Perplexity, and Claude, Generative Engine Optimization is positioned to become the new playbook for brand visibility. It’s not about gaming the algorithm — it’s about being cited by it.
The brands that win in GEO won’t just appear in AI responses. They’ll shape them.” — a16z.

Which brings us to this article: if AI tools are now answering more questions than search engines, how are they doing it in real time?

Intro: The Shift That’s Reshaping Web Search Behavior

This tectonic shift has even led some to declare the premature death of SEO. Others see it as irrelevant altogether, now that more and more searches are taking place within LLM interfaces like ChatGPT. However, what’s really happening beneath the surface is an evolution of search itself—one that ultimately benefits users. AI tools increasingly prioritize quality, relevance, trust, and authority over keyword-stuffed blog posts and manipulative optimization tactics.

There’s a quiet revolution happening in how people look for information online. Instead of turning to traditional search engines like Google, millions are now asking ChatGPT, Claude, Perplexity, and other AI tools to give them answers directly. This shift in user behavior isn’t just cosmetic—it changes who controls visibility, what content gets surfaced, and how creators and publishers benefit (or don’t).

But there’s one key question few are asking: Where does this real-time data actually come from?

If AI tools are giving answers that seem current, timely, and specific, how are they pulling that off if they’re trained on static data?

So, how do LLMs like ChatGPT, Gemini, and Perplexity pull off real-time answers in a world of static training? Let’s break down the three main sources they rely on, how the pipeline works behind the scenes, and what creators and publishers must do to stay visible in an AI-driven search world.

Credit: ML6

1. Search Engines: The First Line of Real-Time Retrieval

When LLMs like ChatGPT offer real-time responses, they’re often using a search engine as a backend. For example, ChatGPT with browsing uses the Bing Search API to query the web just like a user would. It then reads the top few results, extracts relevant content, and summarizes the answer in natural language.

This means the traditional ranking layer—Bing’s algorithm—still determines which websites LLMs see first. If your content isn’t ranking well there, it’s unlikely to be surfaced or cited by the AI.

Credit: ML6

2. Specialized APIs: Structured Data in Real-Time

For example, when you ask ChatGPT, “What’s the weather in Tokyo right now?” it doesn’t hallucinate—it fetches live data from OpenWeatherMap or a similar API.

In cases where LLMs need precise, structured, and fast-changing data (like stock prices, weather, or sports scores), they turn to specialized APIs. These include:

• Finance APIs like Alpha Vantage or Yahoo Finance
• Weather APIs like OpenWeatherMap
• News APIs like NewsAPI.org
• Crypto and sports APIs

The LLM doesn’t guess or generate this data—it queries it live, parses the results, and inserts them into the response.

3. Retrieval-Augmented Generation (RAG): Plugging in Private or Dynamic Knowledge

Imagine a company using a GPT-based assistant to answer internal HR policy questions. Instead of relying on the public internet, the model queries a private database or an indexed document system to retrieve the latest internal policy documents.

Beyond public data, many LLMs use RAG systems to query private or updatable knowledge bases. This is common in enterprise AI, customer support tools, or internal AI agents. The model first retrieves relevant context (from PDFs, databases, or document indexes), then generates an answer based on both its training and the newly retrieved data.

Putting It All Together: How the Pipeline Works

Here’s the simplified flow:

1. User asks a time-sensitive question
2. LLM generates a search/API query and sends it to a real-time source, such as Bing Search API or a specialized external API
3. Real-time sources (Bing, APIs, or internal docs) return relevant data
4. LLM parses, filters, and synthesizes a response
5. The user sees the final answer, often without knowing where it came from

Why This Matters for Publishers and Content Creators

Here’s the bottom line: if you want your content to show up in an AI-generated answer, you need to show up in the sources AI uses. That means:

• Ranking well on Bing (and possibly Google)
• Publishing content that’s clear, high-trust, and easy for machines to parse
• Structuring data in a way that APIs or RAG systems can use

In short: if your content isn’t fetchable, it’s forgettable.

What’s Coming Next: AI Crawlers and Direct Indexing

OpenAI has already launched GPTBot, a crawler visiting websites to possibly build its own index. This hints at a future where LLMs might rely less on Bing or Google and more on their own web snapshots. That’s why it’s critical to control how your content is structured, served, and made accessible to AI systems.

Conclusion: Be Visible to AI, Not Just People

In the AI layer of the web, you don’t win by simply publishing. You win by being retrievable, parsable, and citable. The future of visibility isn’t about keywords—it’s about being the source LLMs want to quote.

Now that you know where LLMs get their real-time data, the only question is: will they find yours?