Prompt design
Construct representative prompt sets that mirror how customers actually ask category and brand questions.
Laboratory · 05
The AI Discoverability Laboratory is our research practice for Generative Engine Optimization (GEO) and citation engineering: the structural, semantic, and authority conditions under which large language models retrieve, quote, and attribute a source. It measures how ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude select and cite brands across category and brand-level prompts.
Focus areas
Methodology
Construct representative prompt sets that mirror how customers actually ask category and brand questions.
Test how content is retrieved, chunked, and cited by generative engines under those prompts.
Refine source architecture, structure, and authority signals to increase citation frequency and accuracy.
Active experiments
Studying how content structure and chunkability influence how frequently a source is cited by answer engines.
Examining which authority signals most consistently move a source into the cited set.
Comparing how ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude select and cite sources for identical prompts.
Applied to
FAQ
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