The Content Optimizer formulates testable claims about ranking drivers, applying Thompson sampling to explore and exploit high-potential hypotheses through targeted text edits. An LLM ranker then repeatedly evaluates these modified snippets against competitor search results across multiple samples, iteratively updating Beta posterior distributions until the draft reaches position one or converges.