Relevance Engineering refers to the technical discipline of specifically structuring content in such a way that AI search systems use it as relevant, trustworthy building blocks for their generated responses — a further development of traditional search engine optimisation, which no longer focuses on a page’s ranking position but on the significance of individual passages in vector-based retrieval systems.
The term was coined in 2025 by Mike King, founder of the US agency iPullRank, and describes the convergence of five previously separate fields: machine learning, information retrieval, content strategy, digital PR and user experience. Relevance Engineering is therefore not a single tactic, but a working model for a search landscape in which clicking on a blue link is no longer the key metric, but rather being cited in an AI response.
Why Relevance Engineering came about
For around 25 years, search engine optimisation followed a consistent logic: a website ranks for a keyword, the user clicks, this generates traffic, and traffic leads to enquiries and revenue. With the rise of generative search systems — Google’s AI Overviews and AI Mode, ChatGPT with its search layer, Perplexity — this chain breaks at a crucial point: the AI answers the question itself, directly within the interface, and at best cites individual sources as evidence. For a growing proportion of searches, the click is no longer required.
This shifts the key strategic question. It is no longer “How do I get to position 1?”, but “How do I become part of the generated answer?”. Relevance Engineering is the answer to this shift: it treats visibility in AI systems not as a matter of chance, but as a plannable, technically sound outcome.
How Relevance Engineering works
AI search systems no longer rely on simple keyword matching, but on dense vector search. Every query, every sub-query and every text passage is translated into a vector — a sequence of numbers that encodes its meaning. The system then searches for spatial proximity between these vectors rather than for matching words. Two texts that convey the same meaning without using a single word in common are situated close to one another in this semantic space.
Furthermore, modern systems process individual passages rather than entire pages. A long page is broken down into text chunks, whose vectors are grouped into thematic clusters; for each aspect of an answer, the system draws on the chunk that is closest to the respective sub-query. For Relevance Engineering, this results in a specific writing principle: content is structured in a self-contained, question-oriented and modular way, so that each passage remains complete and quotable even outside its context.
The three pillars
Relevance Engineering rests on three pillars that must work together:
- Passage / Content: independently quotable blocks of text (often 130–160 words) that provide a complete answer to a clear question and deliver genuine information gain compared to the existing consensus.
- Entity / Structure: Markup with structured data (Schema.org / JSON-LD) so that the machine understands what the elements on a page are, and lists the brand as a standalone entity in its knowledge graph.
- Authority / Signals: external trust signals such as backlinks, brand mentions and digital PR, which determine whether a passage is classified as citable.
A brilliant, unique passage from a domain lacking authority is cited less frequently than an average passage from a recognised source. This is precisely why relevance engineering is explicitly more than just ‘writing better’.
Implications for e-commerce and B2B SMEs
In the B2B sector in particular, search queries very often trigger AI responses. Anyone who runs an online shop or is responsible for the visibility of a small or medium-sized enterprise can sense this shift through a simple metric: the declining proportion of sessions that start via a traditional search results page. If your own brand remains invisible in the AI response, the customer is lost before they have even seen the shop.
For Shopware merchants, ‘relevance engineering’ means, in concrete terms: clean, fully structured product data; consistent Schema.org markup (such as Product, Offer, AggregateRating), server-side rendered storefronts so that AI crawlers can actually read the content, and editorial content that provides a standalone, citable answer for each section. These measures simultaneously benefit both traditional SEO and AI visibility.
A concrete example
A B2B wholesaler wants to be visible for the query “which shop system for B2B wholesale with ERP integration”. Behind the scenes, Google’s AI mode breaks this question down into several sub-questions via Query Fan-Out — B2B functions of shop systems, ERP interfaces, platform comparisons, costs, migration effort. Following the logic of Relevance Engineering, the retailer does not create a single page addressing the original query, but instead produces a standalone, citable passage with genuine added value for each of these sub-queries, marks up the relevant entities with JSON-LD, and provides external evidence of their expertise. This increases the likelihood that their content will appear as a source in the synthesised response — regardless of their traditional ranking position.
Common misconceptions and related terms
A common misconception is that ‘SEO is dead’. Taken literally, this is incorrect: AI search systems do not possess their own truth, but rather synthesise information from the web. Without crawlable, indexed, technically sound content, the models would have nothing from which to construct answers. Traditional SEO is not replaced by relevance engineering, but is absorbed as a technical subset. The old virtues — crawlability, fast delivery, valid structured data — remain essential; they are simply no longer sufficient.
Closely related are Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO), which also aim for visibility in AI responses. Relevance Engineering is the more comprehensive, highly technical and systematic framework that combines these approaches with information retrieval, entity work and digital PR. Methodologically, it touches on fields such as vector embeddings, cosine similarity and semantic scoring.
Outlook
Relevance Engineering is a young field. Many mechanisms are well documented through patents, official announcements and observation, but the exact weightings remain a well-founded hypothesis rather than a fixed formula. Anyone selling an exact ‘citation ranking formula’ is promising a certainty that does not exist. The direction, however, has been clear for years: away from the page as the unit of optimisation, towards the passage; away from the click as the sole metric, towards mentions, citations and the proportion of AI responses. Companies that embed this logic early on in their editorial, technical and PR departments will secure a head start before the competition catches up. A well-founded overview of the discipline is provided, amongst other sources, by the coverage from Search Engine Land; the theoretical foundations are covered in the Wikipedia article on Information Retrieval.
Relevance Engineering in practice
Relevance Engineering is not a catalogue of tricks, but a repeatable working method. In practice, it does not begin with the purchase of a tool, but with a sober assessment of the current situation: where does one’s own brand appear in AI responses today — not in rankings? To do this, you ask Google AI Mode, ChatGPT and Perplexity precisely the questions for which you want to be found, and record whether and how you are mentioned.
In the second step, the most important pages are read ‘like a machine’: Does each section contain a standalone, quotable answer, or is it all a narrative flow from which no clear chunk can be extracted? Next, you check whether the key entities are tagged with structured data, and ask the honest ‘information gain’ question: What on the page does the machine not yet know? These three findings form the basis of a roadmap that brings together editorial, technical and PR teams — precisely the combination that the term ‘engineering’ refers to.
Typical measures include: structuring pages around a clear main question and several precise sub-questions; providing each sub-question with a passage of text that can be read independently; marking up entities and relationships using JSON-LD; and enriching each passage with at least one idea that hasn’t already been written by ten other people.
The new metrics
The dashboard also changes with the optimisation unit. If a large proportion of AI queries end without a click, the sessions curve no longer measures the impact, but only the shrinking remainder. A decline in traffic may even mean that you have become more successful, because the AI is displaying your own answer directly. Pure click and ranking metrics are therefore being replaced by new key performance indicators:
- Share of Voice in AI responses: How often and to what extent is the brand mentioned in relation to relevant topics?
- Citation frequency: How often does your own domain appear as a linked source?
- Entity recognition: Is the company listed as a separate entity in the knowledge graph?
- AI referral traffic: Visitors coming directly from ChatGPT or Perplexity, not via traditional search.
In practice, this requires a deliberate monitoring routine that regularly poses the same purchase-relevant questions to the major AI systems and records whether and how the brand appears. Those who set this up early will see the shift in their own figures before it manifests as a dip in turnover.
Frequently asked questions about relevance engineering
Is relevance engineering the same as SEO?
No, but it does include SEO. Traditional SEO optimises pages for search engine rankings. Relevance Engineering optimises individual passages for use in AI-generated responses and supplements this with entity work, information gain and authority signals. A solid technical SEO foundation remains a prerequisite.
Who coined the term?
Mike King, founder of the agency iPullRank, introduced ‘Relevance Engineering’ in 2025 as a new operating model for search. King was named “Search Marketer of the Year” in the same year and was involved in the publication of the Google Search Leaks.
What role does structured data play?
Structured data according to Schema.org is the language a website uses to tell search engines what the content of a page means. It helps AI systems recognise a company or product as a distinct entity and increases the likelihood that individual passages will be correctly attributed and cited.
How do you measure the success of relevance engineering?
Instead of just clicks and rankings, new metrics are coming to the fore: the proportion of AI responses in which a brand is mentioned (share of voice), the frequency of citations, recognition as an entity, and referral traffic directly from AI tools such as ChatGPT or Perplexity.
Is Relevance Engineering already worthwhile for small and medium-sized enterprises?
Yes. Searches for B2B technology queries, in particular, very often trigger AI responses. As Relevance Engineering is built on the same technical foundations as good SEO, the additional effort involved is manageable, whilst the benefits of getting started early are significant.