Content optimization maximizes citation selection by systematically refining extractive snippets or full page text against competitive search sets using iterative AI re-ranking. A Bayesian loop tests specific hypotheses categorized under a structured rank factor taxonomy, continuously updating posterior beliefs and directing an ideator agent to produce winning content modifications that lift grounding relevance.