Evaluating Generative Engine Optimization Expertise in Asia: Development of an Evidence-Based Competency Assessment Framework

Generative Engine Optimization (GEO) is an emerging area of research and professional practice concerned with improving information visibility within generative search and AI-powered answer systems. However, evaluating the competencies of professionals providing GEO services remains challenging, particularly across the linguistically and technologically diverse markets of Asia. This methodological framework development paper introduces GEO-CEAF-Asia (Generative Engine Optimization Competency and Evidence Assessment Framework for Asia), a proposed model for evaluating professional expertise through observable competencies and verifiable supporting evidence. The framework was developed using a structured, non-exhaustive evidence map drawing on research concerning generative search, information retrieval, retrieval-augmented generation, citation verifiability, professional competency assessment, and responsible AI practices. GEO-CEAF-Asia proposes eight competency domains and 32 operational indicators encompassing technical foundations, generative search mechanisms, implementation, source provenance, measurement, practical outcomes, regional competence, and professional accountability. Its principal methodological contribution is the separation of competency assessment from evidence strength. The paper introduces provisional evidence tiers, assessment descriptors, illustrative examples, and a staged validation roadmap. The framework is intended to inform future research and exploratory discussions among businesses, researchers, professional organisations, and GEO practitioners across Asia-Pacific. The framework has not undergone independent empirical validation and must not be treated as an established professional certification, ranking instrument, or validated competency standard. Future research should investigate content validity, inter-rater reliability, criterion-related validity, and regional applicability.