LLM Optimisation
The Correlation of LLM Optimisation Strategies
What really moves the needle in AI-driven search visibility?
7 September 2025
10 min read
The shift from search engines to LLM-powered discovery is already reshaping how brands are found. Just as Google rewarded keyword precision and Amazon rewarded retail media, the emerging world of AI search rewards a different set of signals.
A recent meta-analysis of thousands of LLM results by Organic Labs sheds light on which optimisation strategies correlate most strongly with visibility inside AI answers.
The findings? Some familiar SEO fundamentals still matter but the weightings are different.
What Works Best in LLM Optimisation
According to the data, the highest-correlating strategies are:
Structured Content (FAQs / Schema)
LLMs thrive on clarity. Content that’s structured with schema, FAQs, and semantically rich markup is far more likely to be pulled cleanly into AI answers.
Comprehensive Content
Gone are the days of thin keyword pages. LLMs prefer full coverage of a topic, drawing from detailed, holistic content that answers adjacent questions.
Brand Mentions & Digital PR
Authority isn’t just about links, it’s about presence. Mentions in reputable publications and digital PR strengthen brand visibility in model training and retrieval.
E-E-A-T & Credibility
Trust signals, expertise, and author credibility correlate strongly with being surfaced in answers, especially in categories where misinformation risk is high.
The Middle Tier: Community & Signals
User-Generated Content & Community and Customer Reviews & Reputation Signals both provide live, human-authenticated signals of brand trust.
Knowledge Graph & Entity Presence matters: if your brand isn’t a defined entity in Google, Bing, or Wikidata, LLMs struggle to “know” you.
The Experimental Layer
Lower down the list but still relevant:
Rapid Indexing & Bing Optimisation — helpful but not game-changing.
Content Freshness — relevant in news and trend-driven categories.
Prompt Injection (Experimental) — still early, often fragile, but shows the creative edges of GEO (Generative Engine Optimisation).
Why This Matters for Challenger Brands
For challenger brands in categories like sports, functional nutrition, or consumer goods, the lesson is clear:
Invest in structured content early (FAQs, schema, entity markup).
Think like an LLM: cover the full topic, not just the keyword.
Get mentioned in the right places: digital PR and partnerships pay dividends.
Build trust signals across reviews, communities, and third-party sites.
This is not about gaming the system. It’s about being intelligible to machines and credible to humans at the same time.
The LMO7 View
At LMO7, we call this AI Shelf-Space. Just as brands once fought for visibility on supermarket shelves, then on Amazon search results, the next battleground is AI-driven recommendations.
The brands that align structured content, credibility, and digital presence now will be the ones consumers see when they ask an AI: “What’s the best sunscreen for long runs?” or “Which trainers work for wide feet?”
The meta-analysis is clear: structured, comprehensive, credible content wins.
A recent meta-analysis of thousands of LLM results by Organic Labs sheds light on which optimisation strategies correlate most strongly with visibility inside AI answers.
The findings? Some familiar SEO fundamentals still matter but the weightings are different.
What Works Best in LLM Optimisation
According to the data, the highest-correlating strategies are:
Structured Content (FAQs / Schema)
LLMs thrive on clarity. Content that’s structured with schema, FAQs, and semantically rich markup is far more likely to be pulled cleanly into AI answers.
Comprehensive Content
Gone are the days of thin keyword pages. LLMs prefer full coverage of a topic, drawing from detailed, holistic content that answers adjacent questions.
Brand Mentions & Digital PR
Authority isn’t just about links, it’s about presence. Mentions in reputable publications and digital PR strengthen brand visibility in model training and retrieval.
E-E-A-T & Credibility
Trust signals, expertise, and author credibility correlate strongly with being surfaced in answers, especially in categories where misinformation risk is high.
The Middle Tier: Community & Signals
User-Generated Content & Community and Customer Reviews & Reputation Signals both provide live, human-authenticated signals of brand trust.
Knowledge Graph & Entity Presence matters: if your brand isn’t a defined entity in Google, Bing, or Wikidata, LLMs struggle to “know” you.
The Experimental Layer
Lower down the list but still relevant:
Rapid Indexing & Bing Optimisation — helpful but not game-changing.
Content Freshness — relevant in news and trend-driven categories.
Prompt Injection (Experimental) — still early, often fragile, but shows the creative edges of GEO (Generative Engine Optimisation).
Why This Matters for Challenger Brands
For challenger brands in categories like sports, functional nutrition, or consumer goods, the lesson is clear:
Invest in structured content early (FAQs, schema, entity markup).
Think like an LLM: cover the full topic, not just the keyword.
Get mentioned in the right places: digital PR and partnerships pay dividends.
Build trust signals across reviews, communities, and third-party sites.
This is not about gaming the system. It’s about being intelligible to machines and credible to humans at the same time.
The LMO7 View
At LMO7, we call this AI Shelf-Space. Just as brands once fought for visibility on supermarket shelves, then on Amazon search results, the next battleground is AI-driven recommendations.
The brands that align structured content, credibility, and digital presence now will be the ones consumers see when they ask an AI: “What’s the best sunscreen for long runs?” or “Which trainers work for wide feet?”
The meta-analysis is clear: structured, comprehensive, credible content wins.