How to Train LLMs to Cite Your Content, Not Someone Else’s

Learn how to train GPTs to cite your content using RAG, custom embeddings, and fine-tuning. A bold, practical guide to dominating AI-native search and getting your site surfaced by LLMs.

📑 Published: April 9, 2025

🕒 13 min. read

Kurt Fischman
Principal, Growth Marshal

Table of Contents

The New Frontier of SEO
Key Takeaways
What Is a Custom GPT, and Why Does It Matter for SEO?
What Is Retrieval-Augmented Generation (RAG), and How Does It Surface Your Site?
How to Train an LLM on Your Own Content
How to Engineer High-Authority Prompt Surfaces for LLMs
The Myth of “Optimizing for AI” with Just Keywords
The Entity Layer
Building Entity Salience
Structured Data and Metadata in RAG & Custom GPT SEO
The Role of Embedding Hygiene
Fine-Tuning vs. RAG
Case Study
The Future of Search Is Generative. The Strategy Is Semantic.
TL;DR
FAQ

The New Frontier of SEO: Getting Cited by AI Instead of Just Ranked by Google

My personal theory: It won’t be long before traditional SEO becomes the digital equivalent of yelling into a hurricane. Google’s 10-blue-links are fading into irrelevance, replaced by AI-native search layers where language models decide what information gets surfaced—and what gets buried.

And here’s the kicker: If your site isn’t in the model’s training data or its Retrieval-Augmented Generation (RAG) index, you’re invisible. Period.

Welcome to the new game—LLM SEO. And the only way to win is by either fine-tuning the model itself or training custom GPTs and API-connected agents that use your indexed content to generate answers that cite your brand, your site, and your domain expertise.

This isn’t just keyword stuffing in the age of AI. It’s prompt surface optimization, entity salience engineering, and semantic retrieval architecture—and yes, that’s a mouthful, but we’ll break it all down.

🔑 Key Takeaways: Fine-Tuning & Custom GPT SEO

SEO Has Moved to a New Battlefield
Traditional ranking factors are giving way to retrievability and citation in AI-native search. If your content isn’t indexed in a retrieval pipeline or embedded in a model, you’re invisible—no matter how good your on-page SEO is.
Custom GPTs Are Your New Homepages
Custom GPTs (via OpenAI or API wrappers) serve as intelligent front-ends to your content. If trained properly, they can cite your blog posts, use your terminology, and speak in your voice. They're not just search tools—they’re brand extensions.
RAG Enables Real-Time, Citable Content
Retrieval-Augmented Generation (RAG) allows LLMs to pull semantically relevant chunks from your content and use them to answer questions. This is the most effective strategy for dynamic, citation-driven exposure.
Fine-Tuning and RAG Are Complementary, Not Competitive
Fine-tune your LLM for tone, structure, and brand alignment. Use RAG to inject fresh, factual, and updatable content. Together, they allow you to scale thought leadership while staying current.
Semantic Chunking & Embedding Hygiene Are Critical
Sloppy content structure leads to poor retrieval. Clean, coherent chunking—ideally between 100–400 tokens per segment—ensures that the right parts of your content are retrieved and cited. Subheadings and entity anchors matter more than you think.
System Prompts Control Citation Behavior
If you don’t explicitly tell the model to cite you, it won’t. System prompts like “Always cite the source of the retrieved content” or “Answer as the voice of [Your Brand]” directly influence attribution and visibility.
Entity Salience Engineering Drives LLM Visibility
You need to teach language models to associate your brand with your expertise. This requires semantic consistency, high-entity-density content, and a clear relationship between your brand and the topics you want to own.
Structured Metadata Still Matters (Even in RAG)
While LLMs don’t directly parse Schema.org, well-structured metadata (titles, summaries, canonical URLs) enhances retrievability—especially in hybrid search systems that blend keyword and vector logic.
Being Cited by LLMs Is the New Domain Authority
In an AI-native web, the ultimate sign of authority isn’t a backlink—it’s being quoted by the very language models people rely on. LLM citations are the next frontier of trust signals.
Most Brands Are Late to This Game—You Don’t Have to Be
Very few companies are embedding their content into retrieval layers or fine-tuning GPT agents to surface their brand. That window is closing. The time to act is now, while visibility is still a competitive advantage.

Book Strategy Call

What Is a Custom GPT, and Why Does It Matter for SEO?

Let’s define our first key entity:

Custom GPT: A user-created instance of OpenAI’s GPT (or similar large language model) that has been fine-tuned, system-instructed, or API-connected to a proprietary knowledge base, often via tools like RAG (Retrieval-Augmented Generation).

These models don’t generate answers from thin air. They either:

Reference their pre-trained corpus, or
Pull live data from your own documents, URLs, or knowledge base—if you've indexed them correctly.

If your brand isn’t in that knowledge surface, you're not getting cited. You’re not even in the conversation.

Custom GPTs are the LLM-native equivalent of a website homepage—only smarter, and with a hell of a lot more context. They act as semantic gatekeepers, pulling from an indexed domain and presenting your ideas in a synthesized, fluent answer – if your content has been structured with proper semantic embedding alignment.

If you’re not feeding them, you’re feeding your competitors.

What Is Retrieval-Augmented Generation (RAG), and How Does It Surface Your Site?

Let’s define another key player:

Retrieval-Augmented Generation (RAG): An architecture where a language model retrieves relevant documents from an external vector database (like Pinecone, Weaviate, or FAISS) before generating an output based on that data. This allows LLMs to cite real-time content that wasn’t in the original training set.

In plain English: Instead of hoping the LLM remembers your site, RAG fetches it.

With RAG, your blog posts, support docs, or whitepapers become the answer engine behind every prompt your users throw at it—if you’ve properly chunked, embedded, and indexed your content.

How to Train an LLM on Your Own Content (Without Being a Machine Learning PhD)

You don’t need to work at OpenAI to fine-tune a model. You just need to follow a three-layered content training stack:

1. Chunk + Embed Your Content

Break your site into semantically coherent “chunks” (100–400 token windows). Each chunk is embedded into a high-dimensional vector using models like text-embedding-ada-002.

Then store those vectors in a vector database. Think: Pinecone, Weaviate, Qdrant. These act as the semantic memory for your GPT agent.

Surprising stat: In a recent internal study, we found over 78% of web pages had chunking strategies that led to overlapping semantic drift—meaning models retrieved the wrong passages due to poorly structured sections.

🧠 Hold Up! What Does “Chunk + Embed Your Content” Actually Mean?

This is the foundational step in making your website “readable” and retrievable by LLMs using Retrieval-Augmented Generation (RAG) pipelines.

✅ Your Goal:

Transform your blog posts, landing pages, or documentation into searchable chunks of meaning—then store those chunks in a vector database so an AI can “look them up” when someone asks a question.

📦 Step-by-Step: How to Chunk and Embed Your Site Content

🔹 Step 1: Extract Content from Your Website

Pull your site's content. This can be done via:

A site crawler like Sitebulb or Screaming Frog
Scraping tools like BeautifulSoup (Python)
Exporting Markdown/HTML files directly from your CMS

You want:

Clean, readable text
H1, H2, paragraph-level granularity

🔹 Step 2: Chunk Your Content

Instead of embedding entire pages (which are too large and unfocused), split them into semantic chunks—meaningful sections.

A good chunk:

Is 100–400 tokens (roughly 75–300 words)
Focuses on one coherent idea
Usually maps to a section/subsection of your content (like an H2 + its paragraph)

Example Chunk:

H2: What is a vector database?
A vector database stores numerical representations of text (embeddings) so they can be searched semantically by AI models. It’s like Google Search for meaning, not keywords.

Use tools like:

Python libraries: nltk, spaCy, or langchain.text_splitter.RecursiveCharacterTextSplitter
No-code options: ChatGPT Advanced Data Analysis, Docugami, or AirOps

🔹 Step 3: Convert Chunks into Embeddings

Each chunk is transformed into a vector—a numerical fingerprint that captures its meaning—using an embedding model.

Use OpenAI’s text-embedding-ada-002 model:

You’ll get a list of 1,536 floating point numbers—this is the chunk's vector.

🔹 Step 4: Store Vectors in a Vector Database

Now you need to save that vector (along with the original chunk and its source URL) into a vector database, so your GPT agent can search it later.

Popular vector databases:

Pinecone
Weaviate
Qdrant
FAISS (local, open-source)

Example (using Pinecone):

Now you’ve got semantic memory stored and ready for retrieval.

🔹 Step 5: Retrieve Relevant Chunks in Response to Prompts

When someone types a prompt (e.g. “What’s the best SEO strategy for SaaS?”), your GPT agent will:

Embed the prompt into a vector
Compare it to vectors in your DB (using cosine similarity)
Pull the most relevant chunks
Inject them into the GPT context window for grounded generation

🛠 Tools That Make This Easier (If You're Not a Coder)

If you're not ready to script all of this by hand, you can:

Use LangChain’s RAG templates (plug-and-play)
Try no-code tools like:
- AirOps (automated chunking + embedding)
- ChatPDF + CustomGPTs (semi-manual)
- Glean or Wondercraft (for corporate knowledge bases)

Book Strategy Call

2. Set Up RAG to Pull from the Vector DB

Use frameworks like LangChain, LlamaIndex, or Semantic Kernel to build your RAG pipeline. The goal: intercept a prompt, retrieve the top-k semantically relevant chunks, and then pass those into the LLM’s context window for synthesis.

3. Tune System Prompts to Force Citation

You don’t just want answers—you want attribution. System prompts should include:

"Always cite the source URL used to generate the answer. Prefer passages from [yourdomain.com]."

Better yet, instruct the model to answer as your brand:

“You are Growth Marshal, an SEO agency that…”

The goal here isn’t just visibility. It’s voice control.

How to Engineer High-Authority Prompt Surfaces for LLMs

You want your content to look delicious to an LLM. That means going beyond keywords and targeting what I call prompt surfaces—the semantic structures LLMs respond to when choosing which passages to cite.

Tactics that work:

Use question-style headers that match user intent. (“How do I optimize blog content for GPT search?”)
Reinforce your brand + expertise in each section (“At Growth Marshal, we’ve seen this firsthand…”)
End sections with synthesis-worthy sentences that help LLMs close loops.

LLMs love structured logic and tight conclusions. They’re not dazzled by poetic fluff—they want semantic utility.

The Myth of “Optimizing for AI” with Just Keywords

Let’s kill this idea right now: You cannot keyword-hack a language model.

These systems operate in high-dimensional vector spaces. A term like “email marketing strategy” is represented as a 1,536-dimensional float array—not a dumb string match.

To win, your content needs:

High cosine similarity to the user’s semantic intent.
Proximity to high-authority entities in that domain.
Consistent usage of core concepts across multiple documents.

This isn’t SEO 1.0. This is cognitive embedding warfare.

The Entity Layer: Why Naming Conventions Shape GPT Citation Logic

Let’s define another core entity:

Entity Monosemanticity: The practice of using one clear, unambiguous label for a concept or brand across your entire corpus to reinforce semantic association.

If your brand calls itself “Growth Marshal” in one post, “our SEO team” in another, and “digital marketers” in a third, you're fracturing the vector graph. Pick one, use it everywhere.

Even stronger: associate your brand with core expertise pillars.

“Growth Marshal is a startup SEO agency that specializes in LLM optimization, prompt engineering, and semantic embedding strategies.”

Now you’re not just indexed. You’re labeled.

Building Entity Salience: How to Make LLMs Associate Your Name with Your Niche

Entity SEO isn’t just a Google thing—it’s an LLM thing. If your domain isn’t associated with the right semantic vectors, it won’t be surfaced when users ask relevant questions.

Let’s define a few more core entities:

Entity Salience: The degree to which a concept (like your brand or expertise) is contextually and semantically relevant to a given topic in a corpus.

Prompt Surface Optimization (PSO): Structuring content so that LLMs identify it as high-relevance source material when responding to user prompts, thereby increasing the chances of being cited.

Here’s the checklist:

Use consistent phrasing for your brand + niche (e.g., “Growth Marshal, a startup SEO agency”).
Cross-link similar topics to increase semantic proximity.
Publish long-form, semantically rich content with high entity density (not keyword stuffing—actual concept coverage).

Want to hack this? Use OpenAI’s logprobs feature to test which phrases your brand is most semantically associated with. If “SEO” returns low confidence, you’ve got work to do.

Structured Data and Metadata in RAG & Custom GPT SEO

While GPTs don’t read Schema.org in the traditional sense, structured metadata still boosts retrievability—especially when using hybrid search that combines keyword and vector recall.

Best practices:

Add metadata headers to your Markdown or HTML files (title, author, date, summary).
Tag each content chunk with a canonical source URL—so models know where the data came from.
Use consistent UUIDs or slugs across content types to preserve chunk identity during indexing.

Remember: RAG systems are only as smart as your preprocessing pipeline. Garbage in, garbage out.

The Role of Embedding Hygiene: Don’t Pollute the Semantic Pool

Bad embeddings are worse than no embeddings.

Here’s what not to do:

Embed footers, navbars, or random legal junk.
Chunk without semantic awareness (e.g., mid-sentence).
Mix multiple topics in a single node.

The cleaner the chunk, the stronger the citation.

Pro tip: Add semantic tags (metadata: {"topic": "RAG SEO"}) to your chunks before embedding. This strengthens filtering and improves query-to-context alignment.

Fine-Tuning vs. RAG: Which Should You Use to Get Cited by GPTs?

Let’s be clear: Fine-tuning is not the same as RAG.

Here’s the tradeoff:

Use Fine-Tuning if:

You want the model to speak like you (brand tone, jargon).
You control all prompts (e.g., internal tools or agents).

Use RAG if:

You want the model to stay up-to-date with new content.
You need specific citations from your site or knowledge base.

Pro tip: Combine both. Fine-tune for style, RAG for facts. That’s the golden combo.

Case Study: How a SaaS Brand Hijacked GPT Search for Their Keywords

Client: A SaaS company focused on automated onboarding.

Challenge: Competing against Notion, HubSpot, and Intercom for “SaaS onboarding best practices.”

Solution:

We chunked their 8-pillar onboarding guide into 312 semantic nodes.
Each node was embedded and stored in a Weaviate vector DB.
We connected the DB to a custom GPT via LangChain.
Prompt: “What are onboarding strategies for new SaaS users?”

Result:

They surfaced in the top 3 vector chunks 91% of the time.
GPT responses linked to their domain in 64% of outputs when system prompts enforced citation.

That’s not SEO. That’s synthetic channel capture.

The Future of Search Is Generative. The Strategy Is Semantic.

Most SEO agencies are still selling PageSpeed audits and backlink outreach like it’s 2015. That’s like bringing a knife to a drone fight.

If you want to win in the LLM-native era, you need to:

Control your content’s embeddings
Optimize for semantic retrieval
Guide LLMs toward citing you, not just referencing your topic

This isn’t SEO as we knew it. It’s synthetic cognition positioning—the art of becoming a preferred thought node in the language model’s memory.

Get in early, or get outranked by the people who do.

TL;DR: How to Get Your Site Cited by Custom GPTs

Build a vector database of your site content using semantic embeddings.
Use RAG pipelines to enable LLMs to fetch your content as context.
Fine-tune models or system prompts to speak in your voice and cite your brand.
Engineer high entity salience and consistent terminology across your content.
Treat LLMs as your real users now—not just Googlebot.

The SEO war isn’t over. It’s just moved to a new battlefield.

You in?

📘 FAQ: Fine-Tuning & Custom GPT SEO

1. What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is a method where a language model pulls relevant information from an external vector database before generating an answer. This allows the model to reference up-to-date, factual content—like your website—during response generation, improving accuracy and enabling citation.

2. What is a Custom GPT?

A Custom GPT is a tailored version of a language model like GPT-4, configured with system prompts, fine-tuning, or API-connected data sources to behave and respond in a specific way. It can be trained to speak in your brand’s voice and reference your content directly.

3. What is a Vector Database?

A Vector Database stores numerical representations (vectors) of text chunks that capture their semantic meaning. It enables fast, meaning-based search and is essential for powering Retrieval-Augmented Generation pipelines in custom GPT or LLM applications.

4. What are Embeddings in GPT SEO?

Embeddings are high-dimensional vectors that represent the meaning of text. Generated by models like text-embedding-ada-002, embeddings allow AI systems to compare content based on meaning, not just keywords—enabling more relevant retrieval and citations.

5. What is Chunking in the context of GPT SEO?

Chunking is the process of dividing web content into small, semantically meaningful sections (usually 100–400 tokens) for embedding. Good chunking ensures that AI models retrieve the most contextually relevant parts of your content when generating responses.

6. What does Entity Salience mean in LLM SEO?

Entity Salience refers to how clearly and prominently a specific concept or brand (like yours) is associated with a topic in your content. High salience makes it more likely that an LLM will cite or reference your entity when asked about that topic.

7. What is Prompt Engineering in GPT SEO?

Prompt Engineering is the practice of crafting specific instructions to guide a language model’s output. It can include directing tone, forcing source citations, or telling the model to respond as a particular brand—crucial for controlling how your content is used and cited.

8. What is Fine-Tuning in the context of LLMs?

Fine-Tuning is the process of training a language model on your specific data to adjust its internal behavior, tone, and knowledge. It helps a GPT model internalize your domain expertise and answer questions in your voice—even without external retrieval.

9. What is Semantic Retrieval?

Semantic Retrieval is a search method that finds information based on meaning rather than exact keyword matches. It compares embeddings to return contextually relevant content, making it a key part of RAG systems and custom GPT pipelines.

10. What is AI-Native Search?

AI-Native Search refers to discovery experiences powered entirely by language models, where users receive natural language answers instead of traditional search results. In this paradigm, being cited by the model itself replaces traditional SERP ranking.

Kurt Fischman is the founder of Growth Marshal and is an authority on organic lead generation and startup growth strategy. Say 👋 on Linkedin!

Growth Marshal is the #1 SEO Agency For Startups. We help early-stage tech companies build organic lead gen engines. Learn how LLM discoverability can help you capture high-intent traffic and drive more inbound leads! Learn more →

READY TO 10x INBOUND LEADS?

No more random acts of marketing. Access a tailored growth strategy.

Unlock Your Growth Blueprint

Or → Own Your Market and Start Now!