What is relevance engineering?

Relevance Engineering (r17g) is the emerging discipline at the intersection of information retrieval, AI, content strategy, user experience, and digital PR—purpose-built for today’s search landscape. It’s not just SEO—it’s structured, measurable, and platform-agnostic relevance across every search surface.

Why the Evolution Matters

Search is evolving. Google’s AI Overviews, ChatGPT answers, YouTube recommendations—they all use generative and vector-driven models. These systems dig deeper, analyzing semantic clusters and context—not just keywords.

Legacy SEO tactics focused on surface-level signals: title tags, keyword stuffing, and linking. But generative AI requires deeper signals via content structure, topical depth, and user experience. Hence, relevance needs to be engineered—not merely optimized.

Core Elements of Relevance Engineering

1. Information Retrieval (IR)

   • Structuring content so AI systems can deeply understand and retrieve it.

   • Using vector embeddings to quantify semantic alignment with intent. 

2. Content Strategy + Digital PR

   • Prompting authoritative backlinks and placements to strengthen credibility.

   • Framing topical pillars that signal authority and maintain keyword focus. 

3. User Experience (UX)

   • Designing content for scan-readability and extraction—headings, lists, clarity.

   • Ensuring pages are accessible, fast, and mobile-friendly.

4. AI & Semantic Scoring

   • Assigning relevance scores using embeddings and cosine similarity.

   • Quantifying which pages align topically with strategic priorities. 

5. Measurement & Optimization

   • Monitoring performance with both traffic and relevance metrics.

   • Iteratively pruning or enhancing content based on relevance results. 

Real-World Application: Content Audits at Scale

At iPullRank, teams audited thousands of posts using a relevance-engineered process:

• Step 1: Define core topic clusters.

• Step 2: Generate embeddings for each page vs. cluster centroids.

• Step 3: Compute cosine similarity to score relevance.

• Step 4: Mix in SEO data (traffic, freshness).

• Step 5: Classify content: Keep, Refresh, or Discard.

• Step 6: Publish and monitor the impact.

This system allowed them to cut 500+ underperforming posts and boost semantic alignment by 2–3%—all based on data, not human guesswork.

Tools & Frameworks

• Embedding Models: OpenAI, Cohere, Ollama

• IR Engines: Elasticsearch, Solr, OpenSearch

• SEO Platforms: SEMrush, Ahrefs, Google Search Console

• UX Tools: PageSpeed, mobile-speed testing

• Analytics Platforms: GA4, Hotjar, Loggers

Challenges & Considerations

• Cross-Channel Complexity: It must span traditional AND AI-first search surfaces. 

• Data Infrastructure: Requires embedding generation and score calculation at scale.

• Team Skill Sets: Calls for IR engineers, UX experts, data analysts, and content strategists.

• Measuring Relevance: Needs custom tools or advanced SEO platforms to track embedded scores.

The Future of SEO is Relevance Engineering

As Mike King (Search Engine Land) observes, “Relevance Engineering is the future of SEO”. It shifts SEO from guesswork to engineering:

• SEO → Relevance Engineering

• Optimizing for rankings → Structuring for relevance

• Tactical fixes → Strategic, measurable frameworks

Brands that embrace this methodology will dominate AI-driven search surfaces. It’s the roadmap to staying visible—where AI and humans alike find, trust, and engage with your content.