Can someone ELI5 how LLMs actually generate responses? Trying to understand why my content does/doesn't get cited

I’ll try to explain this without the jargon. Here’s how LLMs actually work:

The Basic Idea:

LLMs don’t have a database of answers. They’re giant pattern-matching machines that learned from billions of text examples.

Think of it like this: if you’ve read thousands of cooking recipes, you could probably write a new one that sounds plausible. You’re not copying any specific recipe - you’ve learned patterns about how recipes work.

How response generation works:

1. You ask a question - “What’s the best CRM for small businesses?”
2. The model breaks this into tokens - small pieces of text
3. It predicts what text should come next - based on patterns from training
4. It generates one token at a time - until the response is complete

So where does your content fit in?

Two paths:

Path 1: Training Data Your content may have been included when the model was trained. If so, the model learned patterns from it. But it doesn’t “remember” your content specifically - it absorbed patterns about what sources are authoritative on what topics.

Path 2: Live Retrieval (RAG) Newer systems can search the web in real-time, find relevant content, and use it to generate responses. This is how Perplexity works and how ChatGPT Browse works.

The key insight: LLMs learn what sources tend to appear for what topics, and they replicate those patterns.

Let me add the practical content strategy layer to Kevin’s technical explanation.

From training data perspective:

Your content is most likely to be “learned” by LLMs if:

• It appears in high-quality sources (Wikipedia, news sites, academic papers)
• It’s been syndicated/republished widely
• Other authoritative content references it
• It uses clear, structured language

From live retrieval (RAG) perspective:

Your content is most likely to be retrieved and cited if:

• It ranks well in traditional search (AI systems often use search APIs)
• It directly answers common questions
• It’s structured with clear headings and summaries
• It’s been recently updated (freshness signals)

The practical playbook:

1. Create comprehensive, authoritative content on your topics
2. Get that content referenced by other authoritative sources
3. Structure it so AI systems can easily parse and cite it
4. Monitor whether it’s actually appearing in AI responses with tools like Am I Cited
5. Iterate based on what works

Understanding the tech is helpful, but the actionable takeaway is: be the source that humans and machines both recognize as authoritative on your topic.

One important concept nobody’s mentioned yet: attention mechanisms.

Super simplified version:

When the model generates a response, it “pays attention” to different parts of its input and knowledge. The attention mechanism decides what’s relevant to focus on.

Why this matters for content:

Content that clearly signals “I’m relevant to X topic” gets more attention for X queries. This happens through:

• Clear topic signals in headings
• Explicit topic statements
• Consistent terminology

The attention mechanism doesn’t read like humans. It processes everything at once and weighs relevance mathematically. Content with clear, explicit relevance signals scores higher.

Practical implication:

Don’t be subtle. If your content is about “CRM for small businesses,” say “CRM for small businesses” explicitly. The model needs clear signals to pay attention to your content for those queries.

I work in technical documentation and we’ve been thinking about this a lot.

What we’ve learned about structure:

LLMs tokenize text - they break it into pieces. How your content is structured affects how it gets tokenized and whether complete, useful chunks can be extracted.

Good structure for LLM consumption:

• Heading: “How to configure X”
• First sentence: Direct answer or summary
• Following content: Supporting details

Bad structure:

• Long paragraphs with key info buried
• Important points spread across multiple sections
• Context-dependent statements that don’t work in isolation

The test we use:

Take any section of your content. If a machine extracted just that section, would it make sense and be useful? If yes, it’s LLM-friendly. If no, restructure.

Okay, but what about the “hallucination” problem?

Sometimes ChatGPT mentions our company but gets details wrong. Or it cites us for things we never said.

If the model is pattern-matching, why does it make stuff up about us?

Important point: different LLMs have different training data and different cutoffs.

ChatGPT (GPT-4):

• Training data has a cutoff (used to be 2023, now more recent with browsing)
• Relies heavily on training patterns
• Can use real-time browsing when enabled

Perplexity:

• Real-time web search as primary method
• Less dependent on training data
• More like a search engine that generates answers

Google Gemini:

• Access to Google Search index
• Combines training data with real-time retrieval
• Strong bias toward recently indexed content

Claude:

• Training data similar to ChatGPT
• Now has web search capabilities
• More cautious about making claims

The implication:

Your content strategy needs to work for both paradigms:

• Be in training data (long-term authority)
• Be easily retrievable (short-term visibility)

Different platforms will cite you for different reasons.

Super practical question: is there ANY way to know if our content is in the training data?

Like, can we test whether ChatGPT “knows” about us from training vs. browsing?

This thread has been incredibly helpful. Let me summarize what I’ve learned:

How LLMs generate responses:

• Pattern matching, not database retrieval
• Predicts what text should come next based on training
• Learns associations between topics, sources, and authority

Why some content gets cited:

• Appeared in training data in authoritative contexts
• Is easily retrievable by systems using RAG
• Has clear structure and explicit topic signals
• Associated with authority by human sources (citations, references)

What I can actually do:

• Create comprehensive, clearly structured content
• Get referenced by other authoritative sources
• Use explicit, consistent terminology
• Structure for extraction (each section should stand alone)
• Monitor with tools like Am I Cited and iterate

The technical understanding helps me see that it’s not magic - there are clear patterns that determine visibility. Now I have a framework for why certain strategies work.

Thanks everyone!