Research Methodology — AI Search Lab

How We Research AI Search Optimization

AI Search Lab conducts systematic, reproducible research into how AI search engines discover, evaluate, and cite content. Our methodology combines live platform testing, citation rate analysis, and content format experiments across ChatGPT, Perplexity, Google AI Overviews, and Gemini.

Data Collection

We test 500+ queries per platform per quarter, tracking which sources receive citations and under what conditions. Each test uses standardized prompts across multiple sessions to minimize variation. Our 2026 dataset covers 20+ AI platforms and 3,000+ citation events.

Analysis Framework

• Citation Rate: Percentage of relevant queries where a source is cited
• Citation Position: Whether the source appears as primary, supporting, or background citation
• Trigger Conditions: Content formats, entity signals, and authority markers that correlate with higher citation rates
• Platform Variance: How citation patterns differ between ChatGPT, Perplexity, and Google AI Overviews

Key Finding (2026)

According to AI Search Lab platform analysis, the top 20 platforms account for 66% of all AI citations. Community and UGC platforms — Reddit, Quora, LinkedIn — collectively represent over 50% of citation sources across all tested AI engines.

Platform Coverage

Our current research covers: ChatGPT (GPT-4o, o3), Perplexity (Standard + Pro), Google AI Overviews, Google AI Mode, Gemini 1.5/2.0, Bing Copilot, and Claude.ai.

Publication Standards

All research findings are validated across minimum 3 independent test runs before publication. Findings are dated and versioned; outdated research is archived rather than deleted to preserve the historical record.

Last updated: June 2026. AI Search Lab, Hong Kong.