How to Future-Proof Your SEO Strategy with Relevance Engineering

If algorithm updates over the last few years have shifted how our industry approaches SEO, then Google’s announcements at I/O 2025 will force us to rewrite the playbook and reorient ourselves around the future of search.

Gone are the days when SEO agencies would sell Penguin recovery plans to burned companies and uninitiated marketers. Adapting to the ever-changing search landscape was easier because the curveballs weren’t chaotic enough to change the game. While SEOs have been adjusting to life with AI Overviews infiltrating an increasing number of searches, AI Mode has the potential to shift our industry in another direction.

Here are the main takeaways you should have after reading this article:

• AI Mode represents a potential shift in how Google summarizes and surfaces content to users. We are still in the nascent phase of Google’s rollout, confined to the United States, so seeing how adoption rates increase should be interesting. 
• Relevance Engineering is a framework that builds on semantic SEO and aligns with how LLMs retrieve and surface information to users. 
• SEO could evolve into GEO, Generative Engine Optimization, where getting organic traffic and visibility via LLMs will be focused on passage-level optimization, topical depth, and Brand Authority™ (i.e., the trust and credibility your brand has earned as a reliable, go-to source).
• This article provides a tactical how-to guide to increasing your brand’s visibility in an increasingly competitive AI landscape. 

What is AI Mode?

Google announced that as of May 2025, AI Mode would be available to all users in the United States, with plans for a broader international rollout in the coming months. While organic traffic has faced stiff competition from SERP features and sponsored ads, the wider adoption of LLM chat interfaces & AI Overviews has the potential to siphon even more clicks. Unfortunately, this could significantly reduce visibility for brands that rely on siloed-out pages to capture users’ attention.

AI Mode in Google Search is a radically different approach to traditional SERP listings. It contains AI-generated overviews powered by Google’s Gemini models. The output from AI Mode blends generative responses with traditional web links, product listings, and other elements built to engage users in a fully immersive and, in some cases, overwhelming experience.

The evolution of the search landscape, including features like AI Mode, will cause a significant disruption:

• Search results could become AI Summaries rather than the traditional 10 blue links we’ve known since search engines were invented.
• Search will evolve into a post-keyword era, focusing on topical depth and authority rather than ranking individual pages well.
• LLM-powered query expansion will be the foundation for AI Mode’s popularity, answering questions users haven’t thought of before writing a single word.

A common theme here is evolution. When Google rolled out its Penguin and Panda updates over a decade ago, the savviest SEOs adapted by creating more holistic marketing strategies instead of relying on underhanded and outdated tactics (RIP links in blog comments). Yet another watershed moment is on the horizon for the SEO community: we need to think beyond traditional SEO concepts and optimize for discoverability. While SEO’s scope seems to be focused on search engines, Generative Engine Optimization (GEO) encompasses LLMs

But without a proper methodology, it’s just another name.

Enter Relevance Engineering, a new framework that blends semantic SEO, topically deep and citation-worthy content architecture, and AI comprehension.

Relevance Engineering is the evolution of semantic SEO tailored for LLM-based retrieval, interpretation, and content summarization. Unlike traditional SEO, which focuses on building authority for specific web pages, Relevance Engineering isn’t susceptible to “get ranked quick” schemes and general manipulation (at least not yet).

Three core principles help define Relevance Engineering:

• Passage-level optimization: LLMs extract and synthesize relevant passages across the web instead of retrieving full web pages from an index. Each section of your content should address a specific user intent or question.
• Semantic similarity: Topical depth and context matter more than optimization for specific keyword phrases. For example, “most reliable Bluetooth headphones under $100” packs a lot more context beyond the words themselves:
  • Affordability: The upper-limit cost threshold is $100, so results will weed out premium products from brands such as Sony, Bose, etc.
  • Reliability: Although the user is cost-conscious, they expect headphones to last longer than the typical wear-and-tear cycle.
  • Bluetooth Headphones: The user prefers wireless connections, so results may emphasize products with stronger connectivity than those with negative feedback.
• Citation likelihood: Unfortunately, the run-of-the-mill ad-heavy affiliate blog is unlikely to be featured in responses curated by LLMs. Well-structured and authoritative content, while being citation-worthy, is more likely to be included in AI-generated summaries.
• Brand Authority: LLMs tend to cite brands that users recognize as trustworthy or authoritative. Building your authority through consistent quality, original research, and a track record of relevance can increase the likelihood that your brand is cited among industry-leading publications. In an increasingly competitive search landscape, having a notable brand can only increase your chances of being visible in AI summaries. 

Before you start saying, “Well, JB, isn’t this just SEO?” Yes, this is the foundation of semantic SEO, which stresses creating helpful content and ensuring you have topical authority and depth, with the added nuance you need to develop content with AI systems in mind.

I may sound like a broken record, but topical authority has far more potential for LLMs than “highly optimized” pages focused on exact-match keyword phrases.