LLM Search and Its Impact on SEO Strategies

LLM search represents a fundamental shift in how users find information online—potentially as significant as the transition from print to digital media. As AI-powered search engines increasingly generate direct answers rather than traditional “ten blue links,” marketing leaders and SEO professionals must adapt their strategies to remain visible in this evolving landscape.

What is LLM-Based Search?

LLM (Large Language Model) search integrates advanced AI models like GPT (OpenAI), Gemini (Google), Claude (Anthropic), and others with search technologies to deliver more conversational, context-aware results. Unlike traditional keyword-based search engines, LLM search systems:

Process natural language queries and understand user intent
Generate comprehensive answers directly in search results
Incorporate multi-step reasoning for complex questions (e.g., “Plan a budget weekend getaway”)
Combine vector search with semantic understanding
Present information conversationally rather than as a list of links

This technology powers tools like Perplexity, ChatGPT’s browse feature, and Google’s AI Overview, fundamentally changing how users interact with search.

Key Differences Between Traditional Search and LLM Search

Traditional Search	LLM Search
Keyword-focused	Intent and conversation-focused
Returns websites to visit	Often provides direct answers
Link-based authority	Verification and citation-based trust
2-minute average session	6-minute average session
”Ten blue links” format	Conversational interface

The contrast is stark: traditional search is transactional, while LLM search creates an ongoing dialogue with users. Consider how differently these systems handle a query like “Which coffee machine is best under $200?” Traditional search returns a list of review websites, while an LLM might directly compare models, explain key features, and even anticipate follow-up questions about maintenance or coffee bean selection.

A 3D cartoon-style illustration showing two rounded green geckos in a digital landscape split down the middle: on the left, the gecko examines a traditional web browser with a list of 'ten blue links' in neon orange text; on the right, the other gecko interacts with a speech bubble interface displaying a conversational answer in neon orange. The background is a soft blue-to-purple gradient.

The Impact on Search Traffic

The shift toward LLM search presents both challenges and opportunities for websites:

58% of consumers now use generative AI for product/service recommendations (up from 25% in 2023), according to Harvard Business Review
AI-driven overviews reduce clicks to websites by up to 34.5%, as documented in ContentGecko’s research
Projections suggest a 25% drop in traditional search engine use by 2026
LLM search could drive 75% of revenue by 2028, according to Exploding Topics

During the 2024 holiday season, retail sites experienced a 1,300% surge in AI search referrals, signaling a significant shift in consumer behavior. This trend underscores why marketing leaders need to develop strategies specifically for LLM visibility rather than relying solely on traditional SEO techniques.

How LLMs Are Reshaping SEO Fundamentals

1. Content Strategy Evolution

Intent over keywords: Focus shifts from keyword density to answering specific questions
Conversational content: Natural, dialogue-oriented writing performs better
Authority signals: Credentialed authors and transparent sourcing matter more
Question-answer format: Structuring content around FAQs improves LLM retrieval

For example, rather than optimizing a page for “best running shoes,” structure it around specific questions like “What are the most cushioned running shoes for marathon training?” or “Which running shoes are best for people with flat feet?” This approach aligns with how users naturally interact with LLM search systems.

2. Technical Optimization

Schema markup: Enhanced structured data helps LLMs interpret content correctly
Natural language optimization: Prioritize conversational, user-centric writing
Mobile and voice optimization: LLMs better interpret nuanced voice queries
E-A-T enhancement: Expertise, Authority, and Trust signals become more crucial

Schema markup becomes particularly important as it helps LLMs understand the context and relationships within your content. For instance, properly marking up a recipe with cooking times, ingredients, and nutritional information makes it more likely that an LLM will surface your content when answering related cooking questions.

3. Measurement Changes

Traditional SEO metrics like keyword rankings become less relevant as LLM search grows. Instead, focus on:

Citation frequency in AI responses
Traffic from AI search engines
Content retrieval rate for specific queries
Conversion paths from AI search touchpoints

Tools like ContentGecko help marketing leaders track these new metrics and develop content strategies optimized for LLM visibility. With ChatGPT seeing 2.63 billion monthly visits, monitoring AI-driven traffic sources becomes increasingly important for comprehensive analytics.

Best Practices for LLM Search Optimization

1. Create Authoritative, Factual Content

LLMs prioritize content that demonstrates clear expertise and factual accuracy. Implement:

Bylines from credentialed experts
Clear attribution and citation of sources
Regular content updates with current information
Transparent methodology sections for research or data-driven content

Consider how The Mayo Clinic has maintained strong visibility in health-related AI search results by leveraging their medical expertise, clearly attributing content to board-certified physicians, and maintaining rigorous fact-checking protocols. This approach signals to LLMs that the content is trustworthy and authoritative.

2. Optimize for Question-Based Queries

LLM search sessions often begin with specific questions rather than keyword phrases:

Structure content using natural questions as headers
Provide direct, concise answers followed by supporting details
Include comparison tables for product or service evaluations
Anticipate follow-up questions in your content structure

For example, if creating content about digital cameras, include sections that directly answer questions like “What’s the difference between DSLR and mirrorless cameras?” followed by a clear comparison table and explanatory text. Then anticipate follow-up questions about specific use cases or price points.

3. Enhance Technical Foundations

Implement comprehensive schema markup for better context understanding
Create logical content hierarchies with clear header structures
Optimize page speed and mobile experience
Ensure content is accessible to both traditional crawlers and LLM systems

Technical optimization remains crucial because LLMs still rely on properly structured data to understand content relationships. For instance, using FAQ schema markup helps LLMs identify question-answer pairs, while product schema helps them understand features, pricing, and availability.

A 3D cartoon-style illustration featuring a green gecko standing next to a large flowchart on a softly rounded floating panel, illuminated with neon orange markers. The chart compares traditional SEO strategies (keywords, links) on one side and LLM-based strategies (questions, citations, schema markup) on the other, with the gecko pointing to the LLM side. Light blue-to-purple gradient background.

4. Leverage LLM Tools for Content Creation

Tools powered by LLMs can help scale content production while maintaining quality:

Automate up to 73% of content workflows, according to Empathy First Media
Generate content briefs based on LLM query analysis
Create FAQ sections targeted at common user questions
Optimize existing content for LLM visibility

Many marketing teams are using LLM tools to analyze search data, identify question patterns, and create comprehensive content that addresses user needs more effectively. This approach can be particularly valuable for updating existing content to better align with how LLM search systems retrieve and present information.

Future Trends in LLM Search

The LLM search landscape continues to evolve rapidly:

Multimodal search: Integration of text, image, and video understanding
Personalization: More tailored results based on user history and context
Source verification: Enhanced citation and attribution systems
Voice integration: Deeper connection between voice assistants and LLM capabilities

Google’s Gemini, for example, already shows the power of multimodal search by analyzing images alongside text queries. A user can upload a photo of ingredients in their refrigerator and ask, “What can I make for dinner with these ingredients?” The system recognizes the items and generates appropriate recipes, demonstrating how far beyond traditional keyword search these systems have evolved.

Marketing leaders who understand these trends can position their content strategies to capture visibility as these technologies mature.

TL;DR

LLM search represents a fundamental shift in how users find information online, moving from link-based results to direct answers. This change requires SEO professionals to adapt by focusing on authoritative content, question-based optimization, enhanced technical foundations, and new measurement metrics. As LLM search could drive 75% of revenue by 2028, marketing leaders need strategies specifically designed for this new paradigm rather than relying solely on traditional SEO techniques.