How AI Search Works: LLMs, Vector Databases and Cosine Similarity Explained

In my latest book Digital Marketing in the Age of AI, I explore how artificial intelligence is completely transforming digital marketing. One of the most fascinating shifts is how AI search, like what powers ChatGPT, Claude, Perplexity or Google’s AI Mode, works behind the scenes.

It’s time to introduce you to a new vocabulary of marketing terms and concepts that are powering marketing now and will be even more prevalent in the years to come!

In this blog post, co-author Timothy Lutes, an AI engineer and senior SEO specialist at Intuitive Websites, and I break down a powerful part of this new AI landscape: how large language models (LLMs) like ChatGPT use vector databases and cosine similarity to search websites and return smart, helpful answers.

You don’t need to be a programmer to get this. We will explain what we’re observing in everyday terms so any business leader, marketer, or curious person can understand.

This is a fast changing field and this blog is meant to be a snapshot of where we are today based on what we have seen with our clients and the available information on AI search.

What you will come to realize in reading this blog is that AI search is pushing marketers to produce better content, be comprehensive in their thought-leadership and better match content to user intent. Companies that do this will gain an advantage in the months to come and grow faster than competitors.

Step One: From Words to Numbers (Embeddings)

At the heart of how AI search works is a big idea: turning words into numbers.

Every word, sentence, or even an entire document can be turned into something called an embedding. Embeddings are a long list of numbers that represent the meaning of the text.

These embeddings are created using machine learning models trained on massive amounts of data. The goal is to group similar ideas together based on meaning, not just keywords.

Imagine each embedding as a dot floating in a giant invisible 3D space, like stars in a galaxy. Words and ideas that are related are placed close together in that space.

For example:

• “Car” and “automobile” will be near each other.
• “Car” and “chocolate cake” will be far apart.

This process of turning text into embeddings is the key first step that allows LLMs to understand what you’re asking and what content might answer you.

Think of this as a shift from keyword research to answer research. Basically, giving website visitors what they want answers to solve their pain or best meet their goals. This approach happens to fit perfectly with what AI search engines want.

Step Two: Storing Embeddings in Vector Databases

Once you have embeddings (these lists of numbers), you need a place to store and search them. That’s where vector databases come in.

A vector database is a special kind of storage system that’s optimized for finding “the most similar thing” to a question or request.

It works like this:

1. All your blogs, website pages, PDFs and all your online content is converted into embeddings.
2. Those embeddings are saved in a vector database.
3. When a user asks a question, their query is also converted into an embedding.
4. The database searches for the closest “matches” using cosine similarity (we’ll get to that next).

 

Step Three: Cosine Similarity – Finding What’s Closest

Now here’s the secret sauce of AI search: cosine similarity.

Cosine similarity is a math formula that calculates how similar two embeddings are.

Think of two arrows pointing out from the center of a circle. If the arrows point in exactly the same direction, they’re a perfect match. If they point in opposite directions, they’re nothing alike.

Cosine similarity gives you a number between -1 and 1:

• 1 = exact match (same meaning)
• 0 = completely unrelated
• -1 = opposite meanings (rare in practice)

AI uses cosine similarity to compare your question embedding to every chunk of content in the vector database and it picks the ones with the highest similarity score. That’s how it can return accurate, relevant answers beyond or in addition to a traditional Google search.

A Real-World Example

Let’s say a prospective customer goes to Google or an AI prompt and types:

“What are the benefits of switching to solar energy?”

Here is a simplified version of what happens behind the scenes in AI search:

1. That question is broken down into tokens understood by the AI and then converted into a numerical embedding.
2. The vector database compares that embedding with thousands of content embeddings from your website.
3. Cosine similarity scores are calculated for each.
4. The top-matching content chunks—based on meaning, not keywords—are returned to answer the question.

When someone asks about “the benefits of solar energy,” AI doesn’t just retrieve articles with the word “solar.” Instead, it digs deep into meaning, fishing out content that truly answers their question.

This is a much more intelligent system than keyword search because it understands intent and context.

In my Vistage talks, webinars and in my new book, I explained how AI search allows customers to have “conversations” with your brand and not just browse website pages.

Why It Matters for Digital Marketing

Why should you care? Because AI search transcends traditional SEO. It’s about interaction, not just navigation. This evolution is often called AEO (Answer Engine Optimization) optimizing your content so AI tools can easily extract direct answers. Another term that is being used for this is GEO (Generative Engine Optimization). AI reads and understands your content, providing instant, meaningful responses.

 

This is where things get exciting for marketers.

Traditional websites require users to click through menus and skim pages, but AI search changes everything:

• It reads your entire website and returns answers instantly.
• It matches questions to content based on meaning, not just keywords.
• It creates an interactive experience that increases engagement and conversions.

This is a game-changer for your content strategy and getting your brand found.

This method also impacts your overall content strategy. It’s about structuring around user intent and delivering personalized, engaging interactions. Forget endless clicking through static pages; AI makes your website a conversational partner.

This is why we recommend clients structure their content around personas, intent, and benefits so LLMs can find the right answers fast. Also, websites should include industry authority, your experience and evidence based-citations to make your work.

Keep in mind, website visits are dropping and brand impressions are growing on many websites. This work to get found in AI search will strengthen your brand and you get found across multiple channels. People who visit your website will be highly qualified and interested in your products and services. 

Why Vector Search for AI Beats Traditional Google Search

Let’s compare:

Feature	Keyword Search (like Google)	AI Search with Vector DB
Relies on exact words?	Yes	No
Understands meaning/context?	Limited	Yes
Personalized to user intent?	No	Yes
Handles typos/synonyms?	Poorly	Very well
Conversational interface?	No	Yes

This shift in how people research is a key reason I wrote in Digital Marketing in the Age of AI: if you want to get found and get heard, you need to structure your content for AI search and not only traditional SEO.

How to Prepare Your Website for Vector-Based AI Search

Here are a few action items you can take from this blog post:

1. Organize Content by Topic and Intent

Break long pages into chunks by purpose (FAQs, guides, listicles, how-to’s, etc.) so they can be embedded cleanly.

2. Write for People First, Not Just Search Engines

AI search tools focus on clarity and benefit-focused language. Avoid jargon and work to answer common questions people have about your brand, products and services.

3. Use Structured Content and Headers

Good use of H1, H2, and paragraphs helps the LLM process your website more accurately, along with comparison tables where possible. Traditional SEO still matters!

4. Optimize for Benefits and Personas

As I’ve written in multiple articles, your content should align with the needs of your ideal customer personas and their user intent.

5. Use AI Tools to Help You Structure Content

Let AI generate first-draft answers, summarize articles and organize FAQ pages based on customer queries, like the most common questions and objections heard by your sales team. Keep in mind, AI engines generate drafts by drawing on existing content and producing an ‘average’ version. To make sure your content is original and valuable, always add your own insights, examples, and perspective.

6. Content Authority

Your content must also follow the guidelines of E.E.A.T. You can learn more about this here.

What Role Does Traditional Google SEO Play in All This?

You might wonder: does this mean SEO is dead?

Not at all, it’s evolving.

Traditional SEO still matters, especially for organic traffic, but AI search has raised the bar for what is expected of website SEO. SEO now includes:

• Structured content that’s easy to embed
• Authoritative sources for better vector matching
• Depth and clarity so LLMs understand your value
• HIgh levels of user intent to best match AI search queries 

In fact, one of the biggest opportunities right now is getting ahead of your competitors by preparing your website for vector AI search.

At Intuitive Websites, we will make sure you have both traditional SEO and AI search covered.

Embrace AI Search Now

We’re moving toward a future where:

• Websites are fully AI-searchable via embedded data.
• Website users talk to AI agents or clones on your website like a live salesperson or for  customer service support.
• Your marketing funnels are guided by intelligent AI assistants.

This will require a new way of thinking about content:

• Every blog post, PDF, and video transcript can become a “knowledge node” in your AI system.
• Every customer interaction is an opportunity to learn and improve the database.

At Intuitive Websites, we offer services to help clients build their own AI marketing minds with custom ChatGPT tools trained on their content and ready to guide users and websites optimized to get found in AI search.

I often tell Vistage members: If your competitor builds this first, you’re at risk of losing traffic and not knowing why.

Final Thoughts

If you’ve made it this far, you now understand how:

• Large Language Models (LLMs) convert questions into vectors.
• Vector databases store content as embeddings.
• Cosine similarity matches questions to answers based on meaning.
• This formula powers AI-driven search and will continue to grow.

It’s an exciting time to be in marketing. If you want your business to stay ahead, start optimizing your website for this new era of conversational, contextual AI search.

Remember: you can control how you structure and clarify your content, but you can’t control how Google or AI tools decide to rank, cite, or summarize it. Focus on getting your content clear and well-organized, and the rest will follow as these technologies keep evolving.

Would you like help making your website ready for AI search?

At Intuitive Websites, we have built a proprietary software tool that does this automatically for our client websites. This tool will use this exact approach to help our clients create smarter, more helpful chatbots, knowledge hubs, and AI-powered websites. The end being more brand exposure matching user intent, which in turn, converts to new customers for websites using these tactics. When you book at time with us, we can demo this software for you.

Also, reach out to Intuitive Websites for a free assessment of how your website is doing in AI search. We will help you find content gaps, get rid of pages hurting your website, re-purpose old content and more, all with the goal of more brand impressions and qualified website visits.

Complete this form: https://intuitivewebsites.com/free-website-review/

Let’s Connect on Zoom

✅ Schedule a Consult with Tom directly
📞 Call/Text: 719-231-6916
🌐 Visit: IntuitiveWebsites.com

FAQs About AI Search

As AI tools like ChatGPT, Claude, Gemini (Google’s AI Mode), and Perplexity become go-to research assistants, marketers are asking how their content can be found and featured in AI-generated responses, not just in Google search.

Here are 10 frequently asked questions (FAQs) we hear about how AI large language models (LLMs) index and use website content in responses: