SEO Relevance Engineering

Transitioning From Traditional SEO to Relevance Engineering: Understanding the Shift in AI Search

It’s critical to understand how Google’s AI Mode is revolutionizing search.

AI Search is more about implied needs – it is less about search demand per keyword or query phrase. It is redefining and expanding basic SEO concepts.

Traditional SEO, once heavily reliant on keywords and basic optimization tactics, is undergoing a rapid evolution. It’s transforming into a more holistic and nuanced approach known as Relevance Engineering. This transformation, driven by advancements in artificial intelligence (AI), machine learning, and a deeper understanding of user intent, is an exciting leap forward in the field of SEO.

Table of Contents

• Comparing Traditional SEO and AI Mode / Relevance Engineering
• AI Mode / Relevance Engineering’s Impact on SEO and Business
• Practical Applications and Skills for Relevance Engineering
  • Understanding the user & their needs
  • Content Creation & Optimization for AI
  • Key prompt engineering skills for SEO
• What are the Implications of AI Mode for SEO Reporting?
  • Logged-in versus logged-out rank tracking
  • Practical advice for viewing bots as the consumer
  • Strategy first, not technology first
  • Become a trusted, rich source for AL models
• SUMMARY: Embracing SEO Relevance Engineering
  

Comparing Traditional SEO and AI Mode / Relevance Engineering

How to use AI to think strategically means building a better SEO strategy for business growth versus harnessing AI.

First, let’s get on common ground by defining AI Mode / Relevance Engineering.

Relevance Engineering is the updated way to get found online, aiming for visibility on any search surface, not just Google’s main results. It merges traditional SEO knowledge with content strategy, UX, AI, and data analysis, reflecting that modern search is AI-powered, multi-dimensional, and dynamic.

Basically, it means meeting the “information need” of the user. I read an article in 2020 by Doug Turnbull and it shaped my thinking.

“A relevance engineer implements information retrieval algorithms that solve user information needs in real time, at scale. A relevance engineer owns components of the discovery process (search, autocomplete, recommendations, etc). They are primarily engineers, though have awareness of algorithms and machine learning techniques as part of their toolbelt.” – What is a Relevance Engineer?

Comparison Point	Traditional SEO	AI Mode / Relevance Engineering
Primary Goal of Search	Focuses on driving visibility and traffic to websites.	Aims to help people meet their information needs by doing the “Googling for you,” often resulting in zero-click behavior where being cited in AI responses matters more than being clicked. It is highly question-answer based.
SEO Landscape: User Journeys	A page, keyword and URL-focused approach.	SEO professionals now need to identify the core entities to be associated with, analyze how users interact with those entities across the entire purchase or information-seeking journey, and then create content tailored to each stage of that user journey. This involves understanding user intent, mapping out the complete user experience, and developing content that supports them from initial awareness to final conversions in the Age of AI.
Personalization	Traditional SEO metrics focus on rank tracking (examples: device, location, language, and login state).	SEOs now focus more on topical relevance, behavioral/platform preferences, researching engagement signals, and personalized testing to improve ROI.
Underlying Information Retrieval Model	Rooted in classic information retrieval (IR), where content generally comes out the same way it goes in, largely relying on sparse retrieval models (examples: TF-IDF and BM25).	Uses generative AI models to locate, reframe, and synthesize content. It leverages dense retrieval models (vector embeddings) and hybrid retrieval, while incorporating reasoning models.It operates probabilistically rather than deterministically.
Content Optimization Level	Primarily focuses on page-level indexing and keyword optimization for the entire page.	Operates on passage-level retrieval, meaning individual sentences or sections of content are selected. Optimization requires semantically dense and LLM-preferred content. It often involves matrixed optimization across multiple subqueries and multisearch AI-generated answers.
Key Content Characteristics for Success	Accessible, indexable, and easy-reading content, typically with a focus on keyword presence.	Requires content that is semantically complete in isolation, is entity-rich, and semantically assistive and Knowledge Graph-aligned. It helps to be structured in scannable chunks, contextualized with intent language, avoids redundancy, is inherently answer-oriented, and includes factual, attributable, and verifiable content.
Query Processing & Interpretation	Generally treats each query entity in isolation.	Employs a “query fan-out” technique (not new), reformulating the original query into a constellation of related subqueries (implicit, comparative, recent, personalized, reformulation, entity-expanded). It uses multi-stage LLM processing and builds reasoning chains to synthesize answers.
User Context & Personalization	Rank tracking often relies on hypothetical users and is less capable of personalizing query results.	Highly personalized results through user embeddings, which are dense vector representations of the user based on their history of behavioral signals (queries, clicks, location, device, Gmail data, etc.). This leads to memory-aware and 1:1 personalized responses.
Desired User Behavior & KPIs	Success is measured by website clicks and traffic.	Success shifts to Share of Voice (SoV) on AI surfaces, brand sentiment, and being citable in generative AI responses. It is more about attribution influence modeling over direct last-click attribution.
Google Search Console (GSC) Insights	Provides some insights into user clicks and impressions for organic search.	Currently offers no direct insights into AI Overviews or AI Mode / Relevance Engineering performance. Traffic from these sources may appear as “Direct” due to noreferrer tags. SEOs want AI-specific reporting within GSC.
Rank Tracking Methodology	Traditional static rank tracking that measures a website’s position in search results for specific keywords based on a standardized, “first-search” user profile.	Rank tracking now considers the dynamic, synthesized, and user-specific nature of AI results. It requires persona-based tracking in a logged-in state to reflect actual user experiences.
Importance of Vector Embeddings	Has only an emerging emphasis on vector embeddings; SEO tools often rely on lexical scoring.	Underpins everything in Google’s current retrieval model. Understanding how content sits in a vector space is crucial for retrieval and citation, as Google measures similarity scores between these vector embeddings.
Content Editing & Optimization Tools	Most SEO content editors operate on sparse retrieval techniques and focus on single keyword targets.	Demands matrixed semantic content editors that can analyze and engineer content across query clusters, by understanding passage-level analysis and embeddings. Such tools are currently lacking in mainstream SEO software.
Query Relevance	Keyword research tools often assume isolated queries. It limits how content matches what a searcher is looking for.	Search has become a session-driven sequence of related questions. They can be generated by the system itself (e.g., query fan-out, DeepSearch, reasoning chains). AI Mode / Relevance Engineering better understands how query relevance drives visibility in zero-click environments. It considers queries as a set of vectors versus a single vector, where answers are easily digestible for AI models to synthesize into generative results.
Multimodal Content Strategy	Primarily focused on text-based content.	Natively multimodal, synthesizing experiences from text, audio, video, images, and dynamic visualizations. The format matters as much as the content itself, as Google can transcribe, extract, and remix content across formats.
Overall Industry & Professional Shift	Traditional SEOs may struggle with fundamentally different search paradigms. The time-intensive learning curve of AI features can leave a person feeling short-sighted, static, and misinformed.	Advanced AI SEO requires a significant agile shift from old mindsets and tools. It necessitates a realignment to Relevance Engineering. It demands understanding SERP visibility as a vector and how content is judged by its relevancy to what Google thinks the user “meant”. This involves a strategic transition to considering bots and AI assistants as your information consumers.
Reasoning and Thinking	Rooted in classic information retrieval models and traditional LLMs that often provide direct answers based on their training data.	Model capabilities for advanced reasoning, thinking, and multimodal interactions to address complex questions that previously often required multiple searches. The system doesn’t just rank content; it builds “reasoning chains” to connect user queries to AI-generated responses.
Dynamic & Probabilistic Nature	Traditional SEO, which relies on more static, deterministic models (content goes in, comes out the same way).	AI Mode is dynamic and operates probabilistically. Content is sized up and synthesized. This means the outcome of your optimization efforts isn’t fixed; it involves continuously testing and tweaking based on how AI models interpret and use your content over time.
The competitive landscape of search	Optimizing for traffic and rankings.	Reasoning-Driven Retrieval fundamentally is about competing for machine-mediated relevance and being selected as a trusted source by AI models.

AI Mode / Relevance Engineering’s Impact on SEO and Business

Hill Web Marketing approaches this with a mindset of turning AI challenges into business visibility opportunities.

AI is decoupling SEO and content.

It is changing our former mindsets and SEO tools. It is a general-purpose technology, just like the emergence of flying capabilities. The SEO world will redefine itself around this technology. This involves a strategic optimization of web content for AI Overviews and a transition to regarding “bots as a primary consumer” of your information.

Go beyond checking your keyword and page rank, instead ask: “Does my content show up in AI answers?” Adopt and continually refresh your AI SEO strategy.

Forge ahead if you are overwhelmed; here are more tips for you.

Practical Applications and Skills for Relevance Engineering

Understanding the user & their needs (Foundational Actions):

1. Research and meet the real need behind your audience’s queries.
2. Be visible and helpful for comprehensive user goals and contexts.
3. Understand local intent. AI Mode displays a strong tendency to incorporate local context and personalize answers based on user embeddings and behaviors.
4. Implement structured data to add meaning and reduce ambiguity.

Content Creation & Optimization for AI:

1. Write succinct passages with complete ideas.
2. Create content that aligns with how AI reasoning chains answer questions.
3. For AI Mode success, do the hard work to get seen and drive revenue.
4. Help AI bots explain, compare, and guide the user to a satisfying search experience.

With the largest portion of AI-generated search responses going to healthcare-related queries, health organizations with question-answering rich content can outperform their competition.

Leveraging AI tools & prompt engineering:

It takes a human mind to strategically use any of the following:

• Claude
• Gemini
• ChatGPT
• DeepSeek
• SearchGPT
• Grock
• Perplexity

Key prompt engineering skills for SEO:

• Context: Specify the target audience, clearly state the search intent, and explain your desired outcome.
• Define the roles involved: Are you creating a draft for the content writer? Are you seeking a call to action suggestion for your Conversion Rate Optimization specialist? What is your role in the project, and what role is the AI tool to have?
• Interview your AI tool: Example: ask Gemini to ask you questions that will fine-tune your AI project.
• Identify and articulate the task: Example: Use GPT sheets for an outline of your workflow. Content preparation for AI shopping experiences is vastly different from SEO tasks for informational queries.
• Use AI voice mode: to gain another perspective on your AI output before using.
• Leverage the AI’s capabilities for deeper insights: Turn the tables on your AI tools by asking them for your ROI wording to defend the investment.
• Hit refresh: Let the AI tool refresh and gain a fresh human perspective.
• Refine and iterate.

What are the Implications of AI Mode for SEO Reporting?

Google’s owned real estate seeks to predict all initial user questions and relevant follow-up questions that people might have. This throws a kink in our traditional SEO tracking and reporting.

Logged-in versus logged-out rank tracking:

It has taken me a bit to understand this. Tracking rankings in a logged-in, persona-based manner is significantly more challenging, time-consuming, and requires more sophisticated tools and methodologies than traditional static, logged-out rank tracking.

Our established tools and approaches of traditional SEO are often insufficient for the realities of AI-driven search reporting. Those that are agile to learn and leverage more comprehensive tools quickly are ahead.

While the vast majority of everyday searchers don’t consciously think about whether they are “logged in” when they conduct a search, it matters to SEOs. For many people, being logged into their Google account is their default state, especially if they use Gmail, YouTube, or Android devices.

They simply open their browser or the Google app and search. They experience the personalized results without necessarily realizing why they are seeing what they are seeing, or how it might differ for someone else.

The challenge and the need for “logged-in state” tracking isn’t about the user’s awareness; it’s about the system’s operation. Google’s AI Mode / Relevance Engineering is designed to be highly personalized based on that logged-in state. Therefore, for anyone trying to understand, measure, and influence visibility (whether you call it SEO or Relevance Engineering), ignoring the personalized, logged-in experience is lacking. It means you’re not seeing what a significant portion of your actual audience is seeing.

So, while users don’t dwell on their login status, the implication of their logged-in state on the search results they receive is profound. This is what necessitates a more complex tracking approach for professionals in the field. It underscores the shift from optimizing for a single, universal rank to influencing visibility within a dynamic, personalized information retrieval system.

Practical advice for viewing bots as the consumer

It requires viewing AI models not just as tools, but as primary consumers of information, necessitating a strategic approach. For me, it means an updated content marketing strategy to ensure visibility and influence in the age of generative search. The future of online visibility belongs to those who effectively engineer for relevance in this AI intelligent ecosystem.

Strategy first, not technology first

The implications of AI Mode can change every aspect of your business workflow.

There is a huge need for AI-driven leaders who think bigger. Leadership is casting a vision for the future. It’s not about using AI as a replacement; it should rather be an assistant.

Think in terms of “How can AI help me do this?” instead of “How can AI do this for me?” The difference is vast in terms of you knowing what was accomplished and why. Often, people who use AI to write or strategize for them don’t know what was said or accomplished a few days later.

Human “thinking and review” is vital for an effective strategy.

Become a trusted, rich source for AL models

As the above table represents, the landscape of search is undergoing a profound transformation. Whether or not you are prepared, how search works has moved beyond the keyword-centric approach of Traditional SEO to the dynamic, AI-driven paradigm of Relevance Engineering.

As this analysis highlights, success is no longer solely about ranking for specific terms or driving clicks; it’s about becoming a trusted, semantically rich source that AI models can effectively utilize to quickly satisy complex user information needs.

This shift demands a fundamental change in SEOs mindset. It demands moving away from static page optimization towards dynamic content designed for passage-level retrieval and probabilistic AI interpretation.

SUMMARY: Embracing SEO Relevance Engineering

While challenges remain, particularly in measurement and tooling, embracing Relevance Engineering – focusing on semantic completeness, entity alignment, answer-oriented content, and understanding the user journey through the lens of AI-driven query processing – is crucial.

Find out how you visible you are in AI Mode by requesting our SERP Analysis

I want to personally thank Mike King of ipullrank.com for speaking and writing on the topic. He continaully inspires me.