Why does AI search results change every time? Understanding deviations on LLMs
Sep 3, 2025
Lalith Venkatesh
Founding team @ Radix
Have you ever searched for "best payroll software" on ChatGPT and gotten completely different results than your colleague? You're not imagining things. AI search results are deliberately inconsistent, and understanding why could transform your digital marketing strategy.
After analyzing over 1,300 identical searches across ChatGPT, Perplexity, and Google AI, we've discovered the hidden patterns behind AI search variability. This comprehensive guide reveals how AI engines recommend products and how businesses can adapt their SEO strategies for this new reality.
Tl;dr
Ranking on AI platforms is probabilistic, not deterministic like traditional SEO. Instead of fixed positions, you have a probability of appearing based on multiple factors.
Success requires identifying all the key factors that influence your topic area and ensuring your brand has strong presence across those factors to maximize your appearance probability.
How does AI engines recommend products?
From our analysis of comparing the same exact match query triggered from the same exact location.
We found AI engines rank brands on a three-tier basis that is determined by a factors relevant to the entity being searched. Unlike traditional Google search with fixed rankings, Ranking on AI platforms is probability-based.
The Three-Tier AI Visibility System
Tier 1: The Dominant Few (Top 30% of available positions)
Appears in 75-80% of relevant searches
Includes category leaders like Salesforce for CRM, Stripe for payments
Represents the "safe" answers AI platforms default to
Extremely difficult to break into without significant market presence
What drives Tier 1 dominance:
Comprehensive training data saturation - mentioned across virtually all industry content AI was trained on
Omnichannel authority presence - recognized in analyst reports, academic research, news coverage, user forums, and technical documentation
Unbreakable product-concept associations - AI cannot conceive of the category without mentioning them (HubSpot = inbound marketing, Zoom = video conferencing)
Tier 2: The Competitive Arena (Middle 50% of available positions)
Appears randomly in 30-60% of searches
Where most mid-market companies compete
Visibility depends on query phrasing, timing, and context
Offers the best opportunity for optimization
What drives Tier 2 success:
Strong SEO foundation plus community engagement - high search rankings combined with active presence in professional communities and forums
Niche topic authority and positioning - recognized expertise for specific segments or use cases (Pipedrive = sales CRM for SMBs, Notion = collaborative workspaces)
Tier 3: The Occasional Mentions (Bottom 20% of available positions)
Appears once in every 20+ searches
Limited visibility despite having quality solutions
Often overlooked due to insufficient AI training data
What drives Tier 3 appearances:
Ultra-specific use case content - detailed listicles, niche comparison pages, and specialized product documentation that addresses particular scenarios
Authentic grassroots recommendations - genuine Reddit discussions, YouTube tutorials, and community posts that provided real value to users' specific problems
How does LLM results vary based on the query?
Query specificity directly impacts result consistency across all AI platforms. Understanding this relationship is crucial for optimizing your content strategy.
The Specificity-Consistency Correlation
High Specificity Queries (25-30% variation)
Example: "Product A vs Product B for SaaS companies over $50M revenue"
Include specific constraints, company sizes, or use cases
Generate stable, predictable results
Best for capturing high-intent prospects
Medium Specificity Queries (45-55% variation)
Example: "best subscription management software for B2B"
Allow some interpretation room
Mix of consistent and rotating results
Ideal for broader market capture
Low Specificity Queries (65-75% variation)
Example: "payment processing solutions"
Maximum interpretation flexibility
Highly unpredictable results
Difficult to optimize for consistently
How does each AI search engine show variance?
Different AI platforms exhibit distinct behavioral patterns that affect brand visibility. Understanding these differences helps prioritize your optimization efforts.
Google AI: Maximum Variance in responses.
Characteristics:
Widest response variations (150-1,000+ words)
15-20 different citation sources per topic
Prioritizes recent content (3-6 months)
85% consistency for market leaders only
Optimization Strategy:
Focus on recent, high-quality content creation
Diversify your citation sources
Target long-tail, specific queries
Maintain consistent publication schedule
Perplexity: Structured Consistency Approach
Characteristics:
8-10 citation sources per response
40% source consistency across searches
Top 3 recommendations stable in 70% of results
Prefers authoritative, data-driven content
Optimization Strategy:
Build relationships with frequently cited sources
Create comprehensive comparison content
Focus on data-rich articles and case studies
Optimize for positions 1-3 in your category
ChatGPT: Content Structure Optimization
Characteristics:
Most consistent response structure (80% similarity)
Word count varies by only ±30%
Prefers technical documentation
60% brand variation for category queries
Optimization Strategy:
Create well-structured, technical content
Use numbered lists and clear headers
Focus on educational rather than promotional content
Establish external dominance on Reddit and Wikipedia.
What Happens to Citations and Sources Across Multiple AI Searches?
AI platforms use a two-layer citation system that affects how your content gets referenced and discovered.
Understanding the Citation Hierarchy
Stable Foundation Layer (30% of citations)
Official vendor websites, major review platforms, products with high topic authority.
Appears in 70% of relevant responses
Examples: company homepages, G2, Capterra
Essential for consistent visibility.
Variable Citation Layer (70% of citations)
Rotates dramatically between searches
Includes blogs, case studies, news articles
Changes based on recency and context
Opportunity for emerging brands
Citation Source Examples by Query
For "automated revenue recognition tools," we observed:
Search 1 Citations:
Official vendor sites (stable)
Forbes financial tech articles (variable)
Industry-specific blogs (variable)
Search 2 Citations:
Same vendor sites (stable)
Gartner research reports (variable)
LinkedIn expert posts (variable)
Search 3 Citations:
Same vendor sites (stable)
Reddit finance communities (variable)
Case study websites (variable)
How AI Personalizes Search Results: The Technical Mechanics
AI search platforms use three core systems that work together to create personalized, context-aware responses that explain why identical searches produce different results.
Entity Relationship Mapping
How AI connects concepts to brands:
Primary associations - Direct product-to-brand connections (CRM → Salesforce, Payments → Stripe)
Secondary clusters - Related concept networks (Project Management → Team Collaboration → Workflow Automation → Asana)
Strength weighting - Brands with stronger entity relationships appear more frequently
Cross-pollination - Search for "team productivity" can surface "project management" tools due to entity overlap
Training data density - Brands mentioned across more contexts have stronger entity relationships
Temperature Controls and Response Variation
How AI decides between safe vs. creative recommendations:
Low temperature (0.1-0.3) - Conservative responses favoring market leaders like Salesforce, Microsoft
Medium temperature (0.4-0.7) - Balanced mix of established and emerging solutions
High temperature (0.8-1.0) - Creative responses that surface lesser-known alternatives
Category influence - Enterprise software typically uses lower temperatures than creative tools
Query complexity - More specific questions trigger lower temperatures for accuracy
Personalization and Context Factors
How location, history, and behavior shape results:
Geographic Personalization:
Regional preferences - Indian users see Zoho/Tally, US users get QuickBooks/Salesforce
Local integrations - Payment methods (UPI vs ACH), tax systems, regulatory compliance
Market maturity - Emerging markets get more affordable/localized solutions
Conversation Context Bleeding:
Prior enterprise queries bias toward enterprise solutions in subsequent searches
SMB discussions influence recommendations toward smaller business tools
Technical depth in previous conversations affects explanation complexity
Industry mentions prime AI for sector-specific recommendations
Implicit User Signals:
Query sophistication indicates expertise level and influences recommendation depth
Follow-up questions reveal specific use cases and refine suggestions
Response engagement patterns shape future recommendation styles
Time and frequency of searches influence result diversity
How Can Businesses Adapt Their SEO Strategy for AI Search?
Traditional SEO metrics become irrelevant when search positions regenerate with each query. Success requires understanding probability-based visibility rather than fixed rankings.
The New AI Search Optimization Framework
Step 1: Discover Your Target Queries
Research Methods:
Analyze Google Search Console for question-based queries
Mine Reddit discussions in your industry
Study "People Also Ask" sections
Interview your sales team about common questions
Focus Areas:
Find how prospects actually phrase questions
Regional variations in query language
Industry-specific terminology
Pain point-based searches
Step 2: Audit Your Current AI Visibility Across Personalization Contexts
Testing Protocol:
Execute each priority query 10+ times across platforms
Test from different geographic locations (VPN recommended)
Vary conversation history context before searches
Test at different times of day and days of week
Use different devices and user personas
Calculate your appearance probability across contexts
Multi-Context Testing:
Geographic Testing: Same query from US, UK, India, Australia locations
Context Priming: Test after enterprise vs. SMB conversation contexts
Temperature Variation: Note when responses are conservative vs. creative
Persona Testing: Business owner vs. technical implementer perspectives
Visibility Classification:
Core Squad: 75%+ appearance rate across contexts (excellent)
Rotation Pool: 30-60% appearance with context variance (good opportunity)
Forgotten Masses: <30% appearance, highly context-dependent (needs improvement)
Step 3: Map Your Winning Content Patterns
Analysis Framework:
Document when you achieve high visibility
Identify common positioning elements
Track which content types perform best
Note successful feature emphasis
Pattern Recognition:
What topics put you in the Core Squad?
Which content formats get cited most?
When do you appear in top 3 recommendations?
What differentiators get mentioned?
Step 4: Build Citation Authority
Content Strategy:
Create comprehensive resource hubs
Develop industry-specific case studies
Publish regular research reports
Build relationships with industry publications
Technical Implementation:
Optimize for featured snippets
Structure content with clear hierarchies
Use schema markup for better understanding
Implement comprehensive internal linking
What Does the Future Hold for AI Search Optimization?
The shift from deterministic to probabilistic search represents a fundamental change in digital marketing. Businesses must adapt from "ranking for keywords" to "building categorical inevitability."
Key Success Factors for 2025 and Beyond
Categorical Dominance Strategy:
Become synonymous with your solution category
Build thought leadership through consistent content
Establish expertise across all relevant subtopics
Create content that feels essential to cite
Systematic Visibility Tracking:
Monitor AI search results across platforms
Track probability changes over time
Identify successful optimization patterns
Adjust strategy based on visibility data
Multi-Platform Optimization:
Tailor content for each AI platform's preferences
Diversify citation sources and content types
Build presence across the stable foundation layer
Create platform-specific content strategies
Ready to Optimize for AI Search?
The age of fixed search rankings is over. In this new probabilistic landscape, understanding your visibility odds across AI platforms isn't just helpful—it's essential for competitive survival.
Success in AI search requires systematic testing, continuous optimization, and a deep understanding of how these platforms actually work. Companies that master this new reality will dominate their categories in ways traditional SEO never allowed.
Next Steps:
Audit your current AI search visibility using our framework
Identify your Core Squad vs Rotation Pool queries
Develop platform-specific content strategies
Implement systematic visibility tracking
Want to dive deeper into AI search optimization? Contact our team for a comprehensive visibility audit of your brand across all major AI platforms.
About This Research: This analysis is based on 13,622 identical searches across ChatGPT, Perplexity, and Google AI, conducted between May and September 2025. Methodology and detailed findings are available upon request.
