Search is no longer just about matching keywords. Before, it was all about finding pages that contained the exact keywords you entered. But today, search engines focus on understanding the intent and context behind your query, and deliver answers that feel almost human.
Thanks to generative AI in search, we can now receive direct, context-rich answers tailored to their intent, changing the way we discover, consume, and interact with information online. It has been transforming the search experience into something smarter, faster, and more intuitive than ever before.
Author’s Note:
This article is the fourth installment in my ongoing AI + SEO (AEO) series. To get the full picture, I highly recommend checking out the first two parts before diving in:
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How AI Overviews Impact CTR and SEO – Discover how Google’s AI-powered overviews are changing click-through rates and reshaping search strategies.
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Mapping Content to User Goals: A Guide to Intent Orchestration – Learn how to structure content around user intent to maximize engagement and conversions.
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How Generative AI Is Changing Search Behavior – Understand how AI-driven search is reshaping the way people look for and interact with information.
By reading in order, you’ll not only understand what’s changing in search but also learn how to adapt your content strategies step by step.
What is Generative AI in Search?
Generative AI in search is a type of artificial intelligence designed to generate new content rather than simply analyzing or classifying existing data. And when applied to search engines, this means producing original, synthesized answers instead of just ranking and displaying a list of links.
Unlike traditional search, generative AI in search uses advanced systems such as large language models (LLMs). These models are trained on massive datasets and use advanced algorithms to understand context, style, and structure. As a result, these can generate responses that sound natural, conversational, and human-like, instead of robotic or generic.
The big difference is that instead of simply “retrieving” or pulling out an answer word-for-word from a database, AI can understand the intent of a question, adapt its tone, and deliver insights that feel more useful and intuitive to people.
Some of the most visible applications of generative AI in search:
- Google’s Search Generative Experience (SGE): Generates an AI-powered “snapshot” at the top of the results page. This snapshot works by pulling together key information from different sources, then gives you a quick, context-rich answer, which makes you understand a topic faster without clicking through several sites from the search listings.
- ChatGPT Search (OpenAI): This combines real-time web search with the conversational abilities of GPT models. Instead of only relying on pre-trained knowledge, this can fetch up-to-date information from the internet, summarize it in a natural, conversational style, and even provide citations to sources.
- Perplexity AI: This generative AI tool takes a more focused and straightforward approach compared to others. Instead of long summaries or conversational deep dives, Perplexity delivers concise, straight-to-the-point answers.
- Gemini (previously known as Bard): Google’s generative AI chatbot and search assistant, which is designed to produce AI-driven responses that go beyond simple facts. It offers creative content, summaries, and context-aware answers.
- Microsoft’s Bing Copilot: This tool is Microsoft’s version of combining generative AI with traditional search. It is built into Bing search and the Edge browser, which allows users to ask complex, natural-language questions and get back conversational, AI-generated answers instead of just a list of links.
These platforms demonstrate how generative AI is moving beyond theory into everyday tools, redefining user expectations for speed, accuracy, and usability in search.
The Science Behind Generative Search
As we’ve noticed, traditional search engines often struggle with limited contextual understanding, making it difficult to grasp the true intent behind a query. This can result in even irrelevant or frustrating results for users.
But generative AI in search changes the game by using large language models (LLMs) like ChatGPT and Gemini, which are trained on datasets to interpret queries, understand context, and deliver responses that feel natural and human-like. So from keyword dependency in search algorithms, the generative experience relies on these advanced models to understand context and intent, providing more meaningful responses to user queries.
To better understand how this transformation works, it helps to break down the science behind it.
Retrieval vs. Synthesis
In generative AI in search, retrieval and synthesis work together to transform how information is delivered.
Retrieval refers to the AI’s ability to find relevant information by pulling relevant documents, data, or sources from a huge knowledge base to look for facts and data points that might answer the query. Think of it as the AI’s research phase: gathering everything it might need.
Traditional search engines actually rely on retrieval, presenting users with lists of links and documents that match keywords.
Synthesis, on the other hand, is what sets generative AI apart. It involves blending the retrieved information into coherent, context-rich answers that directly respond to the user’s intent. So instead of just listing facts or copying text, it organizes the information, explains it in natural language, and provides context. This is what makes AI answers conversational, readable, and intuitive, rather than just a jumble of data.
In essence, generative search systems seamlessly merge retrieval and synthesis: they first gather the most relevant information, then process and combine it into meaningful insights. This not only improves accuracy and relevance but also elevates the user experience, offering answers that are concise, actionable, and tailored to the query.
Understanding Latent Intent
Generative AI doesn’t just look at the exact words in your search query. It tries to understand your latent intent, or the underlying meaning or goal behind your query that goes beyond the keywords that you typed.
So when you enter a query, an LLM can analyze the wording and context to pick up subtle clues, and predict the underlying goal (or the latent intent). Then, it will generate responses that address the hidden intent rather than just the literal keywords.
For example, when a user searches for “best SEO tools 2025,” the literal meaning of the query is simply a list of SEO tools. However, the latent intent goes deeper: the user is likely looking for tools that are up-to-date, reliable, and easy to use, ideally with pros, cons, and recommendations.
Essentially, generative search reads between the lines of user queries to give answers that are actually helpful, rather than just matching keywords. It delivers answers that are context-aware, actionable, and relevant to the users, which makes information easier to understand.
The Mechanics of Query Fan-Out
One key technique behind generative AI in search is query fan-out, which refers to the process that AI uses to break a single user query into multiple related sub-queries to explore different angles and sources of information—some directly derived, others inferred from user context and intent.
So instead of relying on a single, straightforward search, generative AI “fans out” the query into several angles, interpretations, or related questions. The AI gathers a richer set of data points, uncovering insights that may not be immediately obvious from the original query alone.
For example, if the query is “What are the best strategies for increasing website traffic?” generative AI can fan out the query into related sub-queries like:
- “SEO strategies for website traffic”
- “content marketing tips”
- “social media tactics to boost traffic”
- “email marketing strategies for engagement”
Each of these sub-queries collects focused information, and the AI arranges the results into a comprehensive, context-aware response that covers multiple aspects of the original question. These answers are also more contextually relevant than a simple keyword-based search, delivering a user experience that feels thorough, personalized, and intelligently curated.
Transforming Content Planning and Audit Workflows
The rise of generative AI in search also touches on the approach in transforming content planning and audit workflows by leveraging its ability to understand context, latent intent, and user needs.
In content planning, the combination of retrieval and synthesis enables more effective topic ideation, allowing teams to plan around questions and intent clusters rather than focusing solely on keywords. This approach identifies what audiences truly want, guiding the creation of content that is relevant, comprehensive, and strategically aligned with search behavior.
Then, during content audits, AI streamlines the evaluation process by identifying gaps, redundancies, and areas where existing content may not fully satisfy latent intent. With the application of query fan-out and synthesis, it can highlight missing subtopics or perspectives that would enhance coverage.
Key Takeaway
Generative AI is more than just the next step in search. It’s redefining how we discover information in ways that feel natural, intelligent, and deeply personalized. For SEO marketers today, this is actually a toolkit for thriving in the AI-driven era. They can move beyond keyword stuffing and start crafting content that truly aligns with what users seek.
Success in this new landscape means thinking like AI: anticipating intent, covering topics from multiple angles, and continuously refining strategies. Those who embrace these shifts won’t just keep up with change, rather they’ll set the pace for the future of search marketing.
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