Methodologies

Fresh

This content reflects Metehan Alp's published methodologies as of 2025-2026.

Metehan's core methodologies represent original frameworks for AI search optimization. Each methodology addresses a specific dimension of how LLMs discover, rank, cite, and remember content.

Overview

Methodology	Focus	Key Outcome
CiteMET Method	AI share buttons for LLM discovery	Brand citations across AI platforms
CiteMET Part 2: AI Memory Optimization	LLM footprint and memory reinforcement	Persistent brand recall in AI responses
RRF Top-n Playbook	Reciprocal Rank Fusion scoring	Mathematical citation threshold strategy
Query Fan-Out Analysis	Sub-query decomposition mapping	Coverage scoring for AI Mode visibility
AI Overview Optimization	Vertex AI Search approach	Content structure for AI Overviews
AI Prompt Tracking	GSC-to-prompt transformation	Systematic prompt discovery from search data

How These Connect

These methodologies form a layered system. CiteMET handles the distribution and citation layer. RRF provides the mathematical ranking model. Query Fan-Out maps the sub-query landscape. AI Overview Optimization structures content for Google's AI features. Prompt Tracking closes the measurement loop.

Start with CiteMET for the foundational distribution framework, then move to RRF for the ranking math behind citation thresholds.