How Does AI Search Work?

With the rapid adoption of artificial intelligence search tools by consumers, many marketers are shifting strategies and working to understand what’s going on under the hood. “How does AI search work?” is a question that was top of mind as tools like Perplexity began to launch and we saw traffic for client websites rapidly change. The answer to that question? Tools like ChatGPT, Google Gemini and Perplexity actually source information from two places at once. There’s a starting layer of data they were trained on, which is then referenced and compared against relevant results from a search engine like Google. All the training data and search results are then filtered through how an AI search engine understands its audience.

How Does AI Search Work

Imagine you’re a consumer trying to build out a meal plan; one that focuses on upcoming flavor trends but also doesn’t break the bank. Traditional search could provide either a meal plan, a list of trends or options that are affordable. AI search uses training data, web search and user personalization to achieve all three in one response without the consumer ever having to leave an app.

Key Aspects 

• Training Data: the baseline that AI learns from 

• • Usually makes up a large portion of publicly available internet data. 

• • Often out of date since training a new model takes time. Large organizations like OpenAI often update their training data after making partnerships with organizations like Walmart. 

• • Influences the preferences of the AI search tool. For example, Google Gemini is more trained on Reddit. 

• Web search tools: how AI responses stay up to date 

• • Whether it’s Google search or a proprietary search engine, AI largely relies on traditional search methods to pull the most current information. 

• • Web search tools vary by tool: Gemini uses a ‘query fanning’ approach that returns results from multiple variations of what you search for, synthesizing them into a coherent whole. 

• • Results from these tools also vary based on company priorities. Perplexity leans toward a mix of sources from forums to industry news sources. ChatGPT prefers national news and company websites, while Claude uses a relatively even mix. 

• User personalization: shapes your results before you ever see them 

• • Every interaction a user has with an AI search tool shapes the results they’ll get in the future. 

• • Location, demographics, choice of words, tone of voice, political preference, favorite sources, etc. all play a role in the response an AI drafts to a user prompt. 

• • Even when not logged in, AI search records your preferences and provides content accordingly.

In short, AI search is a comparison engine with a lot of reviewing and tweaking occurring before a consumer ever sees a response to their search. Aspects such as preferred sourcing deals in the case of the Washington Post and Perplexity’s Comet browser or whether a user lives in cold or warm weather have tremendous impact on what information is ranked in a response or AI overview.

AI Search Compared to Traditional Search Engines

Using our meal planning example above: Consumers largely prefer AI search for a few key reasons:  

• Answers and augments complex questions 

• • In meal planning for example, the combination of prior user searches such as “What are some great gluten free foods for a toddler” or “Is there a farmers market by me” will automatically be considered when asked “help me build a meal plan that includes trending foods but also doesn’t break the bank.” 

• Responds naturally with human-like content 

• • The technologies operating behind the scenes ensure that AI responses sound incredibly human, even when covering complex topics. This makes AI search tools much more usable than many search engines as there’s no need to learn the correct way to search in order to find the information you want. 

• Understands user intent 

• • AI tools are able to build comprehension through understanding the relationships between words and concepts. This ability means ChatGPT has a better chance at understanding what a user actually wants, not just the keywords being searched for. This is especially impactful in niche industries where complex terminology and low search volumes prevent results from indexing well in traditional search. 

• Ranking based on different signals 

• • As we covered on our What is Generative Engine Optimization (GEO) blog, audience signals are shifting toward interactive paragraphs. With this comes a new focus — AI searches throw traditional page rankings out the window, with Ahrefs reporting 90% of cited pages in short-tail queries not even showing up in the top 10 for a traditional Google search. Instead, we’re seeing user testimonials, social media posts and PR rise to the top. 

Here’s an example of how this might work if someone were to search “What are top flavor trends in 2026.”  In the video, you can see a Perplexity search result showing a graphic, a brief overview that lists major sources and a numbered list of ingredients. Compare that to a traditional search engine with the exact same query. Common sources appear, but no ingredients are directly named. Which one satisfies user intent more?

Improving Brand Visibility in AI Search Results

You can improve your brand’s visibility in AI search by incorporating the following approach:  

1. 1. Rethink your digital content strategy. Every piece of digital content connected to your brand is fair game for AI search to cite — this includes employee social media posts, your company website and even PR coverage. 
   2. Start building your key AI metrics. This will help you grasp user behavior over time. 
   3. Review your website with a tool like AI Eyes to see what information a crawler actually sees. 
   4. Understand the conversational queries your audience is sending and the intent behind what they are searching. 
   5. Build out your testimonials across Reddit, Quora, industry forums, social and onsite reviews. 
   6. Strengthen your PR efforts with earned media placements in publications that have authority in your industry. 
   7. Shift your website content structure to an answer-first structure by leading with a brief summary answer to your expected user question, then expanding in more detail later on in your blog or press release. 

There’s a lot of legwork you can do ahead of time to make sure your brand is well positioned for the next AI shift. We’re already hearing conversations about the upcoming GPT-6 model for ChatGPT, which will focus even more on personalizing experiences and choosing sources aligned to each individual user. Learn more about what we’re expecting and the approaches you can take over on the Building Brand Gravity podcast. 

We’re working to incorporate the techniques and considerations listed above across all of our client efforts — from product pages in animal health to how pet food brands are referenced by AI tools. Now that you know how AI search works, it’s time to take the next step and actually start deploying GEO best practices. If you’re interested, reach out to our team today.