How is AI changing the world of traditional search? There are a multitude of ways to answer that question, but one of the most important distinctions is how search engines use the information they find.
For decades, search engines had a primary task: Help users find pages that contain the information they’re searching for. AI search, which uses generative AI to create answers to users’ queries, introduces another job. A search engine can rank pages for a user to explore, while AI search can construct an answer and find information that supports that answer.
Content now has to work for two related tasks: Ranking and grounding. Ranking helps determine whether a page is a useful result for a search. Grounding helps determine whether the information on that page can be used as evidence to support an AI-generated answer.
What is Ranking in Traditional Search?
Ranking in traditional search is the process search engines use to determine which webpages are most relevant and useful for a user’s search and in what order they should appear in the search results. Search engines consider many factors when ranking pages, including the relevance, quality, authority, usability, and accessibility of the content.
Think of traditional search like asking a librarian for help with research. You don’t necessarily expect the librarian to answer the question for you. Instead, you expect them to point you toward the books, articles, and other sources most likely to contain the information you need. You are still responsible for getting the information from those sources.
Traditional search provides a list of sources; you, as the user, can then evaluate them to help you find your answer.
What is Grounding for AI Answers?
Grounding is the process of connecting an AI-generated answer to information from external sources, such as webpages, articles, research studies, product documentation, and other information available online, so the answer is supported by evidence rather than relying only on what the AI already knows. In this context, that means finding relevant information from webpages and using that information to help construct and support an answer for the user.
The role of the search index is evolving from helping rank webpages to also helping support AI-generated answers. This can involve retrieving relevant webpages or passages from the index and providing that information to a generative AI model as additional context when it produces a response. The model can then generate an answer based on both its existing capabilities and the information retrieved for that particular question.
Imagine providing that same librarian a different assignment: Don’t tell me which sources to read. Answer the question for me. The librarian still needs all of the skills required for the first assignment. They need to find relevant sources, evaluate their quality, and understand what each source contains. But now the librarian also takes on some of the work that previously belonged to you. They need to evaluate the information within those sources, determine which pieces of information actually support the answer, and use that evidence to construct a helpful response. The librarian still needs good sources. The difference is that now they also need to find and retrieve the good evidence within those sources and contextualize it in a way that answers your question.
With traditional search, a list of sources is the output. With AI search, those sources can become inputs used to create the summarized (and cited) output.
Why Does Grounding Matter for AI Search?
AI models learn from large amounts of information during training, but that does not mean they know everything or that everything they learned is still accurate. Information changes, new information becomes available, and some questions require specific evidence that an AI may not have encountered during training. Grounding provides a generative AI model with external information it can use when constructing an answer, rather than relying solely on what it learned during training. Just as importantly, grounding can help determine when there isn’t enough reliable evidence to support an answer at all. If the available information is outdated, contradictory, or insufficient, providing no definitive answer may be better than constructing one from weak evidence.
Static Grounding vs. Dynamic Grounding
Grounding can rely on static or dynamic information. Static grounding gives an AI model access to a defined set of reference materials, such as company policies, product manuals, technical documentation, or research papers. For example, a manufacturer could give an AI assistant access to its installation manuals so it can use those instructions when answering contractors’ questions.
Dynamic grounding retrieves information that may change and needs to be accessed when a question is asked. This could include current product availability, prices, weather conditions, news, or newly published and updated webpages. With dynamic grounding, relevant information can be retrieved from the web when a question is asked and provided to the generative AI model to help it construct a current, supported answer.
So, Does Grounding For AI Replace Traditional Search?
No, grounding for AI does not replace traditional search. In many ways, it builds on the same foundation. Before a generative AI search experience can use information to support an answer, relevant, reliable information first needs to be found and retrieved.
Think back to our librarian. Giving the librarian a new assignment does not mean they suddenly stop caring about the quality of the books and articles they consult. They still need to determine whether a source is relevant, trustworthy, current, and appropriate for the question. The difference is that they now have an additional responsibility: identifying the specific information within those sources that can support the answer.
What Makes Information Useful for Grounding?
A page can be relevant to a search without every piece of information on that page being equally useful for grounding an AI answer. When AI search constructs an answer, it needs to determine not only which sources are relevant, but which specific information from those sources can support the answer.
For example, imagine a roofing company publishes a comprehensive guide to replacing a roof. The page might be relevant enough to rank for searches about roof replacement. But if a generative AI search experience is answering the question, “What does it cost to replace an asphalt shingle roof in 2026?” a generally relevant roofing page isn’t necessarily enough. It needs specific, current information that can actually support the answer.
The page will also need context. When was that pricing published or updated? What size roof does it apply to? Does the estimate include labor and materials? Does location affect the cost? That makes qualities such as specificity, context, sourcing, freshness, and clear attribution increasingly important.
How Can You Optimize for Both Ranking and Grounding?
You can, and should, optimize for both ranking and grounding. Your content needs to adhere to E-E-A-T standards and demonstrate trust and credibility, including making it clear who created the content, what expertise they bring to the topic, and what evidence supports the claims being made. Be sure to:
1. Cover the topic of the page thoroughly, while making answers easy to find. Structure content in clear, digestible sections so individual ideas within a larger page are clear, well organized, and easy to identify.
2. Make claims specific and provide support. Specific claims give an AI-generated answer something concrete to work with, while supporting evidence helps establish whether that information is reliable enough to use. For example, instead of simply saying that a product is “long-lasting,” state its typical lifespan, explain what factors can affect it, and cite the source of that information. When you make specific, factual claims, touting relevant credentials and citing reputable sources can make the origin and support for that information clearer.
3. Utilize original information and tools. Give people, and AI-generated answers, something useful to find on your site that isn’t simply a repetition of information available elsewhere. Original research, first-party data, expert insights, case studies, calculators, comparison tools, and other resources unique to your organization can provide information that other sources cannot. This isn’t a new tactic to the search landscape, either. Some marketers refer to it as “information gain,” but it’s also adhering to the “Expertise” tenet in Google’s E-E-A-T standards.
4. Make the sources and the context of the information clear. Traditional search can present several results and allow the user to compare them. An AI-generated answer, however, may need to account for those differences before presenting a single response. In our roofing example, one source might estimate a different replacement cost than another because the sources use different locations, materials, roof sizes, or labor costs. Making that context clear can help prevent information from being used in a way that changes its original meaning.
5. Keep pages current with up-to-date information. Freshness takes on additional importance when information may be used to construct an answer. Outdated information is not simply an old search result; it could become evidence supporting an inaccurate answer. When updating content, it’s always helpful to note when a page was last updated, which can help indicate how fresh and recent this information is.
Create Pages Worth Ranking With Information Worth Using
Ranking asks whether the page deserves to be shown. Grounding adds another question: Does the information within that page deserve to be used to support this particular answer? Overall: Do not sacrifice the human experience to chase AI citations. It is possible to structure your information in a way that machines can understand it without sounding like a robot yourself. The fundamentals that make content useful, trustworthy, and worth finding still matter. The parameters of the assignment may be changing, but creating content worth finding, trusting, and using remains the goal. Remember, you’re still crafting content for the human on the other side of the search query.
