Search is changing from a system that mainly returns a list of links into systems that can retrieve information, assess its relevance, and use selected sources to construct an answer.
Traditional search engines have long used information retrieval and ranking systems to determine which documents should appear for a query. Modern AI systems add another layer: large language models (LLMs) can help interpret queries, rank or rerank retrieved information, evaluate relevance, and generate answers from the information they receive.
Research published by Google DeepMind, Microsoft Research, and the wider information-retrieval community shows that these systems are developing quickly, but also that AI retrieval is not simply a replacement for conventional search.
AI information retrieval is the process of finding relevant information from a collection of documents, websites, databases, or other sources and making that information available to an AI system.
A simplified AI search process can look like this:
Not every AI system follows exactly this sequence. Some systems retrieve information before generation, while others may retrieve additional information during the reasoning or generation process.
Retrieval-Augmented Generation (RAG) is one important example. A RAG system retrieves external information relevant to a user’s question and provides that information to an LLM before or during answer generation. Research on RAG has developed rapidly because external retrieval can give models access to information that is newer, more specialized, or outside their original training data.
This distinction matters for SEO because the AI model does not necessarily need to memorize an entire website to use it. A retrieval system can locate a relevant passage when a user asks a related question.
The first challenge is understanding what the user actually wants.
Consider these two searches:
“refrigerator repair Dallas”
“My refrigerator is running but isn’t getting cold. Who can repair it in Dallas?”
They may represent a similar underlying need, but the second query contains substantially more information about the user’s situation.
Modern retrieval systems increasingly use semantic understanding rather than relying exclusively on exact keyword matches. The objective is to identify information that is relevant to the meaning and intent of the query.
Research into domain-specific retrieval has found that LLM-based retrieval can perform particularly well when queries contain multiple intents or require deeper semantic interpretation. One 2025 study comparing an LLM-powered retrieval approach with Elasticsearch for environmental and earth-science data found higher precision for the LLM-based approach on its evaluation dataset, particularly when handling multiple-intent natural-language queries.
This does not mean traditional keyword retrieval has disappeared. In practical search systems, multiple retrieval techniques can be combined.
The important development is that semantic relevance is becoming increasingly important alongside lexical matching.
Finding potentially relevant documents is only the beginning.
A system may retrieve hundreds or thousands of possible documents. It then needs to determine which information should receive greater attention.
This is where ranking and reranking become important.
Traditional information-retrieval systems can use signals such as textual relevance, links, document characteristics, user behavior, and many other factors. Modern AI systems can also use language models to estimate how relevant a document or passage is to a particular query.
Their research describes LLMs taking several roles within information retrieval, including ranking documents, generating synthetic data, and evaluating retrieval systems. Their experiments also examine how interactions between different LLM-based components can affect retrieval effectiveness.
This introduces an important concept for digital marketers:
A page may be available to a retrieval system but still not be considered sufficiently relevant for a particular question.
Imagine an AI system retrieves 50 passages that might answer a question.
Instead of giving all 50 passages equal importance, a second stage can examine those results and determine which passages are most useful.
This process is commonly called reranking.
An LLM may be asked to assess whether a passage actually addresses the user’s question, whether it contains useful information, and how closely it matches the intended task.
For content creators, this has an important implication.
Writing an article that merely contains the right keywords may not be enough.
The content also needs to contain clear, useful information that directly addresses the underlying question.
Evaluation is one of the most interesting developments in AI search.
An AI retrieval system needs some way to determine whether the information it retrieved is useful.
Researchers can evaluate retrieval using measures such as precision, recall, ranking quality, relevance judgments, and other metrics.
This is significant because “relevant” is not always a simple yes-or-no concept.
A document can contain the correct subject but still fail to answer the user’s actual question.
For example, a page about refrigerator repair may mention “refrigerator not cooling” several times while providing little practical information about the possible causes.
Another page may explain the symptom, possible causes, safety considerations, repair options, and when professional service is appropriate.
Both pages may contain the same keyword.
They are not necessarily equally useful.
This creates a new research problem.
If an LLM is used to rank information, another LLM may be used to evaluate whether the ranking was successful.
That sounds efficient, but researchers have identified potential problems.
This means that AI evaluation should not automatically be treated as objective simply because a language model performed it.
Human judgments, carefully designed benchmarks, multiple evaluators, and appropriate metrics can still be important.
An AI model can only work with the information it receives from an external retrieval system when that information is part of the system’s retrieval pipeline.
This creates a simple but important principle:
If a retrieval system fails to find the relevant information, the generation stage may have insufficient evidence to produce a reliable answer.
For website owners, this provides a useful way of thinking about content.
A webpage is not just a document for humans.
Increasingly, its individual pieces of information may become potential retrieval units for AI systems.
There is no universally established “GEO ranking formula.”
However, research into information retrieval provides useful principles.
This does not mean that every paragraph should be written for a machine.
In fact, good AI-oriented content often overlaps with good human-oriented content.
A clear answer that directly addresses a user’s question is useful to both.
One limitation of a model’s internal knowledge is that it may not contain the newest information.
Retrieval can address part of this problem by allowing the system to access external sources.
RAG research describes this as a way of connecting an LLM with external and potentially up-to-date information rather than depending exclusively on information encoded in the model itself.
This is one reason websites can remain important in an AI-search environment.
The role of a website is not necessarily limited to receiving a click.
Its information may become part of the evidence retrieved to answer another person’s question.
That creates a different form of search visibility:
These two forms of visibility can overlap, but they are not identical.
Research is also exploring more dynamic retrieval systems.
A 2025 tutorial on Dynamic and Parametric Retrieval-Augmented Generation describes approaches that move beyond a static retrieve-then-generate process. Dynamic RAG can determine when and what information should be retrieved during the generation process, particularly for tasks requiring multiple steps or changing information needs.
This is important because real questions are not always simple.
“Which refrigerator repair company should I contact if I have a Sub-Zero refrigerator that is leaking and I’m located in Dallas?”
Answering this could require information about:
An AI system capable of combining multiple retrieval steps can approach such questions differently from a conventional keyword search.
The research does not establish a single list of “AI ranking factors” that businesses can simply optimize for.
Instead, it suggests that SEO is becoming part of a larger information ecosystem.
Traditional SEO remains concerned with making content discoverable and relevant to search engines.
AEO focuses more directly on providing useful answers to questions.
GEO looks at visibility within generative search and AI-generated answers.
These concepts overlap, but they should not be treated as interchangeable technical standards.
The underlying information-retrieval research points toward a broader principle:
Content needs to make its useful information easy to discover, interpret, evaluate, and connect with a user’s question.
A conventional SEO approach might focus on:
“refrigerator repair Dallas”
An information-retrieval-oriented approach would go further.
The page could clearly explain:
Now the page contains many individual pieces of useful information.
A user searching for “refrigerator repair Dallas” may need the general service page.
Someone searching for “why is my refrigerator leaking water” may need the troubleshooting information.
Someone searching for “Sub-Zero refrigerator repair Dallas” may need the brand-specific information.
The underlying business is the same, but the retrieval intent is different.
This is one of the most useful ways to think about AEO and GEO without relying on unsupported claims about secret AI ranking factors.
Traditional SEO often encourages marketers to think in terms of pages and keywords.
AI information retrieval encourages a more granular question:
What individual pieces of information does my website contain that could answer real questions?
That shift can influence how businesses create content.
Instead of producing another generic 1,500-word article because a keyword tool suggests it, marketers can start with actual questions customers ask and build pages that contain clear, independently useful answers.
This also makes internal linking more meaningful.
A general appliance-repair page can connect to refrigerator repair, dishwasher repair, brand-specific service pages, troubleshooting guides, warranty information, and local service areas.
The website becomes an interconnected information source rather than a collection of pages created primarily around individual keywords.
There is sometimes an assumption that semantic search means traditional authority signals no longer matter.
The research does not establish that conclusion.
Information retrieval remains concerned with relevance, quality, robustness, evaluation, and other characteristics of retrieved information.
The more defensible conclusion is that AI adds additional layers to information retrieval rather than eliminating the underlying discipline.
A website still needs to be discoverable.
Its information still needs to be relevant.
Its claims should still be trustworthy.
And the information needs to be presented clearly enough for both users and retrieval systems to interpret it.
The research community is moving toward increasingly sophisticated questions:
Can an AI system retrieve the right information?
Can it rank that information appropriately?
Can it determine whether the information is actually relevant?
Can it distinguish reliable evidence from weak information?
Can it evaluate its own retrieval without introducing systematic bias?
Can it retrieve information dynamically when the question requires multiple steps?
These are not merely technical questions for AI engineers.
They are increasingly relevant to publishers, brands, local businesses, SEO professionals, and digital marketers.
As search becomes more conversational and answer-oriented, the organizations that publish clear, useful, well-structured information have more opportunities to participate in the information retrieval process.
AI search should not be understood simply as “Google but with AI.”
It represents a broader evolution in information retrieval.
Traditional search primarily asks:
Which documents should be shown for this query?
Generative search can additionally ask:
Which information should be retrieved, which pieces are relevant, how should they be evaluated, and how can they be combined into a useful response?
Research from Google DeepMind and Microsoft Research shows that LLMs are increasingly being investigated as retrieval rankers, relevance judges, data generators, and assistants. At the same time, researchers continue to identify limitations involving evaluation reliability, bias, retrieval quality, and the interaction between different AI components.
For SEO and digital marketing, the practical takeaway is therefore not that traditional SEO is disappearing.
It is that search visibility is becoming more closely connected to information quality and machine-readable relevance.
The future of search may involve fewer situations where a user simply receives ten links and chooses one.
Instead, AI systems may increasingly retrieve information from multiple sources, evaluate it, synthesize it, and provide an answer.
For businesses and publishers, that makes one question increasingly important:
When an AI system needs information about your subject, does your website contain clear, useful, trustworthy information that it can actually retrieve and understand?
