What this means
Perplexity's answer-engine and AI search ecosystem.
Historical record
Perplexity describes Photon in-house retrieval and ranking engine
The architecture shows how index representation, candidate retrieval, ranking and latency affect the source-discovery pipeline before an answer is generated.
Perplexity introduces Q2D-Web for large-scale retrieval evaluation in agentic RAG
The visible user prompt is not necessarily the query a source competes for. AI visibility therefore depends on several distinct stages: query reformulation, first-stage retrieval, ranking, evidence selection, and eventual citation.
Peer-reviewed study measures attribution gaps between retrieved and cited AI-search sources
It distinguishes retrieved source exposure from visible citation credit, revealing limitations in using citations alone to infer publisher contribution.
Study finds model-specific concentration in AI search news citations
Provider-specific citation concentration and diversity can differ substantially. AI visibility measurement should track source distribution and publisher diversity separately for each model family instead of relying on pooled citation totals.
Perplexity launches its answer engine
It helped establish the answer-engine model that later drove GEO, citation tracking and AI visibility measurement.