Relevance engineering is a systematic approach to improving visibility across all search surfaces through the integration of information retrieval science, content strategy, digital PR, and artificial intelligence.
Mike King's presentation on Relevance Engineering at SEO Week stopped me in my tracks.
After 20+ years in digital marketing, I've seen countless algorithm updates and tactical shifts. This is different. The relevance engineering framework isn't another SEO trend. It's a complete shift in how we think about search visibility in the AI era.
What Relevance Engineering Actually Means
Relevance engineering is a systematic approach to improving visibility across all search surfaces through the integration of information retrieval science, content strategy, digital PR, and artificial intelligence. Mike King created this framework because traditional SEO methods no longer align with how modern search engines operate.
The definition matters: "the art and science of improving visibility for any search surface." Notice what Mike King left out. No mention of SEO. That exclusion signals a fundamental shift in how we think about search optimization and content visibility.
When working with clients now, I ask different questions entirely. How does content appear in AI Overviews? What happens when ChatGPT receives questions about your industry? Are large language models citing your content when generating responses? These questions reflect the new reality where search engines and AI systems determine visibility through semantic understanding rather than keyword matching.
The data confirms this transformation. Nearly 60% of Google search queries result in zero clicks. AI Overviews appear in a significant percentage of queries. When users enter AI mode, traditional SEO, built on keyword density and backlink profiles, fails to account for how search results are actually generated.
The Technical Foundation That Changes Everything
Search engines now use vector embeddings to create mathematical representations of content meaning. This fundamentally changes how relevance engineering approaches optimization compared to traditional SEO methods. The relevance engineering framework treats visibility as an engineering problem requiring an understanding of information retrieval systems.
Vector space models power modern search. Google search creates vector representations of queries and documents as coordinates in a multi-dimensional space. Content saying the same thing produces similar representations regardless of exact keyword matches. Semantic alignment matters more than keyword density for improving visibility.
For my clients, understanding this technical foundation changed everything. We stopped obsessing over exact match phrases. We now engineer content that shows semantic understanding of user intent across the entire user journey.
The practical implication is direct. Content strategy must focus on meaning rather than matching specific keywords. Semantic search evaluates concepts, relationships, and user queries together. Start by mapping the semantic territory around your core topics, then create content that genuinely addresses user behavior and search intent.
How AI Mode and Query Fan-Out Work
Google's AI mode uses query fan-out to decompose a single search into multiple semantically diverse sub-queries before generating responses. This means content must be relevant to expanded search variations, not just head terms. The AI mode query fan-out process represents the most significant change in how search engines process user queries.
The query fan-out process in AI mode works in stages. A user submits a query. Google expands it into multiple related queries. A custom document corpus is formed from the relevant results across all sub-queries. The large language model synthesizes information into a summarized response that appears in AI Overviews.
When auditing client content now, I evaluate whether pages answer multiple related queries. Does the content provide specific, factual information that AI systems want to cite? Build content addressing the semantic variations that query fan-out generates, not just the primary keyword.
How Structured Data Enables AI Understanding
Structured data helps both traditional search engines and AI models understand content at a fundamental level. It enables entity disambiguation and improves how information retrieval systems process and cite your content. This goes far beyond generating rich snippets.
Consider entity disambiguation in practice. When you mark up "Apple" with appropriate schema, AI systems know whether content refers to the fruit or the technology company. This precision improves retrieval accuracy when search engines build their custom document corpus for AI Overviews.
The relationship between knowledge graphs and large language models is evolving through three emerging models. These include Knowledge Graph-enhanced LLMs, LLM-augmented Knowledge Graphs, and Synergized LLMs and Knowledge Graphs. All combine structured precision with flexible neural reasoning capabilities.
Properly structured data increases the likelihood of content being included in knowledge graphs. This makes content more likely to be retrieved and cited by AI systems across multiple search surfaces. Implement comprehensive schema markup for your most important content to maximize visibility in modern search.
How Passage Optimization Improves AI Citations
Modern AI search operates at the passage level rather than the page level. This requires content structured in 50-150 word semantic units that can stand alone when extracted by Retrieval Augmented Generation systems. Each paragraph must function as an independent, citable chunk of information.
The relevance engineering framework emphasizes passage optimization as "semantic scoring in action." It's about structuring content for relevance and extractability at the same time. AI systems retrieve passages from various sources to build complete responses in AI Overviews.
Three questions now guide my content review process. Can this paragraph be understood in isolation? Does it contain specific data points AI can confidently cite? Are relationships between concepts stated explicitly rather than implied through context?
This discipline actually improves content quality for human readers too. Clear paragraphs with explicit relationship statements and specific information serve both AI systems and users effectively. Structure content so each semantic unit provides complete value, then watch citation frequency improve.
Why Digital PR Now Requires Mathematical Precision
Digital PR in the relevance engineering framework focuses on mathematically precise relevance. AI tools can calculate topic alignment rather than just chasing random placements across unrelated publications. This transforms how we build authority for semantic search.
Brand mentions scattered across the internet mean nothing if they lack relevance to core topics. Context matters enormously for search visibility. Mentions in semantically relevant contexts carry far more weight than random appearances when large language models evaluate authority signals.
My digital PR approach shifted accordingly. I target publications with high semantic relevance to client topics. We create assets AI systems specifically want to cite in search results. We build relationships with sources that contribute to knowledge graphs informing AI Overviews.
The practical application is straightforward. Map your semantic territory and identify publications covering those topics with authority. Earn mentions in contexts that strengthen your topical relevance. This builds authority signals that improve visibility across AI search surfaces.
How Google Models E-E-A-T Through Vectors
Google models expertise by creating vector representations of authors and comparing them to other authors. This means E-E-A-T is shown through semantic consistency and depth rather than author bios or credential listings. Mike King's explanation completely changed my understanding.
"EEAT is not about author bios, and it never was," Mike King stated directly. Google vectorizes pages across your site and averages these vectors to represent the entire domain. If you write 100 pages and 60 cover SEO topics, you become mathematically labeled an SEO expert in the vector space model.
Topical authority requires both depth and consistency over time. Off-topic content dilutes your semantic representation and weakens authority signals. The SEO community needs to understand this: building topical relevance means sustained focus on related topics, not scattered content across unrelated subjects.
Apply this insight practically by auditing content for topical coherence. Ensure your site maintains consistent depth in core topics. Remove or consolidate content that dilutes semantic representation. This focused approach to content strategy improves how AI systems evaluate your expertise.
What I've Changed in My Client Work
Since implementing relevance engineering principles, I've transformed how I approach SEO projects through four major changes. These include semantic-centric planning, retrieval simulation testing, new success metrics, and content restructuring based on embedding analysis.
Content planning now starts with semantic mapping rather than keyword research. We identify core topics and map relationships between subtopics. Embedding analysis ensures clusters are semantically coherent. This approach addresses how modern search engines actually process queries through semantic search rather than keyword matching.
Retrieval simulation testing became essential to our process. This means prompting AI systems with queries and analyzing which content gets retrieved. The process reveals optimization opportunities invisible to human reviewers examining content traditionally.
Success metrics evolved significantly. Citation frequency matters more than traditional SEO rankings. I track branded search volume, AI Overviews mentions, and share of voice in AI results across search surfaces.
One client engagement illustrates the content restructuring approach. Semantic analysis identified 45% of blog posts for pruning or consolidation. Site-wide semantic relevance improved measurably after generating embeddings and measuring cosine similarity against core business topics.
Optimizing for Multiple AI Search Platforms
Relevance engineering extends optimization beyond Google search to AI Overviews, ChatGPT, Perplexity, TikTok search, Amazon, and app stores. Each platform has different retrieval mechanisms that prioritize different content characteristics. This multi-platform approach defines modern search visibility.
ChatGPT Search uses distinct retrieval mechanisms from Google. Perplexity emphasizes citation transparency and source attribution in conversational search. Content stating clear facts with accurate data points performs well across all platforms where AI systems generate search results.
One B2B SaaS client engagement demonstrates multi-platform success. Over six months, we significantly improved brand visibility in ChatGPT responses through citation-worthy content combined with strategic digital PR in semantically relevant contexts.
Build your content strategy for multi-platform visibility by ensuring factual accuracy, clear structure, and explicit information AI tools can cite. Monitor visibility across multiple search surfaces and AI mode results rather than focusing exclusively on traditional SEO rankings.
Why Search Has Become a Branding Channel
Search has always been a branding channel, but the SEO community positioned it as performance marketing focused on traffic and clicks. Mike King argues this perspective misses how AI Overviews and zero-click results fundamentally change search visibility value.
With zero-click searches dominating a majority of queries, the goal isn't necessarily receiving clicks. It's being recognized as an authoritative source driving branded awareness. When users see your brand cited repeatedly in AI Overviews, they develop trust without clicking through to your site.
This requires different success metrics for search engine optimization. Track citation frequency across AI platforms. Monitor branded search volume increases. Evaluate sentiment when your brand appears in AI generated content. Measure share of voice for target topics in AI results.
Accept that search is primarily a branding channel in the AI era. When AI mode processes queries, optimize content for citation and recognition rather than just clicks. This mindset shift that Mike King advocates aligns your content strategy with how modern search actually works.
Implementation Challenges Worth Knowing
Adopting relevance engineering requires technical capabilities many organizations lack. This includes understanding of vector embeddings, cosine similarity, and information retrieval concepts from computer science that traditional SEO professionals may not have developed. These barriers are real but surmountable.
Organizational resistance presents ongoing challenges. Clients with established SEO processes sometimes resist fundamental changes to their search engine optimization approach. Explaining relevance engineering concepts to executives unfamiliar with semantic search requires patience.
Measurement complexity adds another layer of difficulty. Traditional SEO offered clear metrics that stakeholders understood. Relevance engineering requires more sophisticated approaches showing success in ways that may be harder to communicate initially.
Start implementation by educating stakeholders on why the search field changed. Focus initial efforts on content restructuring and structured data where results are visible. Build technical capabilities incrementally as the approach proves value.
Where AI Search Is Heading
AI search continues evolving toward voice search, conversational search, and AI agents that break down tasks with minimal human direction. Content must support agent reasoning and action-taking beyond simple information retrieval. This trajectory defines the future of search visibility.
Semantic approaches capture conversational queries effectively. This aligns with emerging voice and AI-driven search trends. Large language models increasingly personalize results based on user behavior and preferences.
Search behavior is no longer linear or universal. Each user sees uniquely generated experiences tailored to history, preferences, and intent interpretation by AI systems. Content must serve diverse intents and contexts across multiple search surfaces.
Prepare now by implementing the relevance engineering framework before the search field shifts further. Build semantic authority in your core topics. Create content structured for AI extraction and citation in AI mode. Position your brand for visibility however search evolves.
The Bottom Line
Relevance engineering represents the most significant evolution in search engine optimization I've witnessed. The relevance engineering approach treats visibility as an engineering problem requiring understanding of semantic search, information retrieval, and how AI systems actually process content. Mike King's relevance engineering framework fundamentally changes how we approach search.
Technical SEO still matters. Core web vitals still matter. Helpful content still matters. But the framework for thinking about these elements has shifted from page-level keyword optimization to engineering content systems that show semantic authority across search surfaces.
Current tools and methods are approximately 10 years behind how Google search actually works. SEO professionals embracing relevance engineering now will build capabilities while competitors remain focused on outdated approaches to traditional SEO.
Make the commitment to learn the relevance engineering framework. The transformation from traditional search to AI-driven discovery through AI mode is already underway. Approach adaptation systematically with the rigor that relevance engineering provides. The results in client work confirm this approach daily.
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