LLM Indexing Myths: What People Get Wrong About 'Training Data'

If you're optimizing for AI the way you optimize for Google, you're working with the wrong mental model.

LLM Definition Box: Large Language Models (LLMs) don't "index" content the way search engines do. They work through two completely different mechanisms: training data (knowledge baked into weights) and real-time retrieval (RAG/web search). Most SEO practitioners conflate these, leading to wasted optimization effort.

Key takeaways:

Training data influences what the model "knows" - frozen at a cutoff date
Retrieval (RAG/web search) determines what gets cited in real-time answers
87% of ChatGPT search citations match Bing's top organic results - ranking matters
The Princeton GEO study found keyword stuffing performs 10% WORSE for AI visibility

The bottom line: Effective GEO strategy requires understanding which mechanism you're targeting - and optimizing accordingly.

The Mental Model Problem

SEO practitioners have spent years mastering the crawler-index-rank model. A Googlebot visits your page, indexes the content, evaluates it against hundreds of ranking factors, and serves it when relevant queries match.

This mental model doesn't map to how LLMs work. At all.

As Britney Muller noted on X: "Y'all are just rediscovering SEO by way of 'AI SEO' or 'GEO'... Everyone's acting like optimizing for LLMs is an entirely new discipline that we need to do ASAP. It's not."

She's right that the fundamentals still matter. But she's also highlighting a critical distinction: you need to understand the mechanisms before you can optimize effectively. Too many practitioners are applying Google-era tactics to a system that works completely differently.

The first step is knowing where you stand. Tracking AI citations across platforms reveals which mechanism is actually driving visibility for your brand - and which optimization tactics are worth your time.

Myth #1: LLMs "Index" Content Like Search Engines

This is the foundational misunderstanding that leads to almost every other mistake.

How search engines work:

Crawler visits your page
Content gets indexed (stored in a searchable database)
Ranking algorithm evaluates relevance and authority
Results served to matching queries

How LLMs work: There is no "index" in the Google sense. Instead, there are two completely separate mechanisms:

Mechanism 1: Training Data (Knowledge in Weights)

During training, the model processes massive amounts of text and encodes patterns into neural network weights. This knowledge is "baked in" - frozen at the training cutoff date. The model doesn't store or retrieve specific documents; it learns statistical patterns about language and concepts.

Reddit has formal licensing agreements with both Google and OpenAI, explicitly providing content for AI training. This means Reddit discussions are literally in the training data, influencing what models "know" about topics.

Mechanism 2: Real-Time Retrieval (RAG/Web Search)

When you use ChatGPT with web search or Perplexity, the model doesn't rely solely on training data. It:

Reformulates your query
Searches an external source (often Bing)
Retrieves relevant documents
Synthesizes an answer with citations

These are fundamentally different processes. Optimizing for one doesn't automatically help the other.

For a deeper look at how ChatGPT specifically handles citations, see our ChatGPT Citations Explained guide.

How Training Data Actually Works

When a model is trained, it processes text from sources like web crawls, books, academic papers, forums, and licensed data. The model learns patterns - linguistic structures, factual associations, reasoning frameworks - and encodes them into weights.

Key characteristics:

Frozen at cutoff: Once training completes, the knowledge is locked. ChatGPT's training data has a cutoff date. It can't learn about events after that date from training alone.

No direct retrieval: The model doesn't "look up" information in a database. It generates responses based on learned patterns. This is why it can be confidently wrong - it's producing statistically likely continuations, not citing stored facts.

Community influence matters: For training data, authentic discussions across diverse platforms have more influence than traditional SEO signals. A product mentioned genuinely across Reddit, Quora, and Stack Overflow carries more training influence than a well-optimized but isolated product page.

Practical implication: To influence what an LLM "knows" about your brand, you need presence in the training corpus before the cutoff. Community mentions matter more than backlinks.

How Real-Time Retrieval Actually Works

Real-time retrieval systems like RAG (Retrieval Augmented Generation) and web search work very differently from training data.

RAG Process:

Your query is converted into a vector embedding
A vector database is searched for semantically similar content
Top-K most relevant chunks are retrieved
These chunks are added to the prompt context
The model generates a response using both its training knowledge and the retrieved content

ChatGPT Web Search Process:

ChatGPT reformulates your query into search terms
Sends the query to Bing
Receives 20-30 top results
Applies its own algorithm to select and cite sources
Synthesizes an answer with inline citations

Critical insight: 87% of SearchGPT citations match Bing's top organic results. If you want ChatGPT to cite you, ranking well in Bing is a direct path.

This is fundamentally different from training influence. You can't influence training data after the cutoff, but you can influence real-time retrieval by ranking in the underlying search system.

For the full picture on how GEO differs from traditional SEO, see GEO vs SEO: What's the Difference.

Myth #2: More Backlinks = Better AI Citations

This myth persists because backlinks are so central to traditional SEO thinking.

The reality is nuanced:

For training data influence, backlinks don't directly matter. What matters is authentic presence across diverse sources that end up in training corpora. A product discussed genuinely in 20 Reddit threads has more training influence than a product with 200 backlinks but no community discussion.

As Britney Muller put it: "Brand mentions are the new backlinks" - at least for training data influence.

For retrieval influence, backlinks matter indirectly because they help you rank in the underlying search engine. If Bing uses backlinks as a ranking factor (it does), then backlinks help you rank in Bing, which helps you get cited by ChatGPT when it searches.

The key shift: Traditional SEO optimizes your domain authority. GEO optimizes your presence across the entire information ecosystem that AI systems sample from.

Myth #3: Keyword Stuffing Helps AI Visibility

This one surprises most SEO practitioners.

The Princeton GEO study tested multiple optimization techniques and found that keyword stuffing performed 10% worse than baseline for AI visibility.

Why? LLMs use semantic understanding, not keyword matching. They understand that "feline companion" and "cat" refer to the same concept. Stuffing keywords actually signals lower-quality content - exactly the opposite of what you want.

What actually works (30-40% improvement according to the same study):

Cite sources: Adding relevant citations from credible sources
Include statistics: Quantitative data instead of qualitative claims
Add quotable statements: Clear, extractable definitions and facts

The study also found domain-specific variation:

Historical content: Authoritative language works best
Factual queries: Citation optimization most effective
Legal/government topics: Statistics provide greatest benefit

Myth #4: Reddit Optimization Guarantees Citations

Reddit's prominence in AI citations led many to treat it as a guaranteed path to visibility. Then the data changed.

What happened: Reddit citations in ChatGPT dropped from 14% to 2% in late 2025.

The cause: Google disabled the num=100 search parameter, limiting ChatGPT's retrieval to top 20 results. Many Reddit threads rank outside the top 20, so they stopped appearing in ChatGPT's citation pool.

The lesson: Citation patterns depend on platform partnerships and retrieval mechanics, not just content quality. Reddit content didn't get worse - the retrieval mechanism changed.

But Perplexity is different: 46.7% of Perplexity's top sources still come from Reddit. Platform-specific strategies matter.

This is why one-size-fits-all GEO advice fails. The optimal strategy depends on which AI platforms you're targeting and how their retrieval systems work.

What Actually Works: The Evidence-Based Approach

Based on the Princeton GEO study and real-world citation analysis, here's what moves the needle:

For Training Data Influence

Build authentic community presence:

Participate genuinely in relevant subreddits, forums, and Q&A sites
Helpful comments with 500+ upvotes carry more signal than promotional posts
Consistent, long-term engagement builds cumulative influence

Target diverse sources:

Don't concentrate on one platform
Spread across Reddit, Quora, Stack Overflow, industry forums
The more diverse your presence, the more robust your training influence

For Retrieval Influence

Optimize for Bing:

87% of ChatGPT citations match Bing's top results
Traditional Bing SEO tactics apply here
Ranking improvements translate directly to citation probability

Structure content for extraction:

Clear definitions in the first 100 words
Q&A format with explicit questions as headers
Standalone sentences that can be quoted directly
Numbered lists for processes

Add evidence:

Citations from authoritative sources (30-40% improvement)
Specific statistics with sources
Expert quotes with attribution

For Both Mechanisms

Focus on quality over quantity:

Keyword stuffing hurts, not helps
Depth and accuracy matter more than volume
Authoritative, well-sourced content wins in both paradigms

Not sure which mechanism matters for your business? An AI visibility audit identifies your current citation patterns and the optimization opportunities you're missing.

FAQ

Does optimizing for Google also help with AI citations?

Partially. 87% of ChatGPT search citations match Bing's top results, and Bing often mirrors Google rankings. But training data influence requires a different approach focused on community presence and authentic discussions - something traditional Google SEO doesn't emphasize.

Can I influence what an LLM "knows" about my brand?

Only before the training cutoff date. After that, your influence is limited to real-time retrieval - which means ranking well in the underlying search systems (primarily Bing for ChatGPT). For future training influence, focus on building diverse, authentic community mentions now.

Why did Reddit citations drop in ChatGPT?

Google disabled the num=100 search parameter in late 2025, limiting ChatGPT's retrieval to top 20 results. Many Reddit threads rank outside that range, so they disappeared from ChatGPT's citation pool. The content quality didn't change - the retrieval mechanics did.

Is GEO just SEO with a new name?

The fundamentals overlap (quality content, authority, relevance), but the mechanisms differ significantly. Traditional SEO optimizes for crawl-index-rank. GEO optimizes for training data influence and retrieval mechanics. As Julian Goldie noted: "It's the evolution of SEO. The fundamentals - quality content, backlinks, and trust - still win." But the application of those fundamentals requires understanding how AI systems actually work.

Should I focus on training data or retrieval?

It depends on your timeline and goals. Training data influence is slow (you're waiting for the next model update) but persistent. Retrieval influence is faster (optimize for Bing today, get cited tomorrow) but volatile (platform changes can shift patterns overnight). Most effective GEO strategies target both.

Does Perplexity work the same as ChatGPT?

No. Different AI platforms have different retrieval systems and citation patterns. Perplexity still pulls 46.7% of citations from Reddit, while ChatGPT dropped Reddit citations dramatically. Platform-specific optimization is necessary.