OFF-GEO: Mitigating Off-Target Visibility in Generative Engine Optimization

Generative Engine Optimization (GEO) methods use large language models (LLMs) to rewrite source content and improve its visibility in generative engine responses. However, these gains can extend to queries that a content provider does not wish to target. Mitigating these undesired gains is challenging because desired and undesired queries may rely on overlapping source content, so reversing GEO edits can affect both. We propose OFF-GEO, a framework that learns a shared editing rule for GEO-optimized sources. We optimize the rule to maximize desired visibility subject to a minimum average reduction in undesired visibility. Candidate rules are compared using visibility changes measured from generative engine responses, while feedback from edits that fail to reduce undesired visibility guides rule refinement. The learned rule guides edits to new optimized sources without query inputs or generative engine feedback. Experiments on E-commerce and GEO-Bench show that OFF-GEO reduces undesired visibility more than desired visibility across all metrics, while keeping desired visibility above that of the original sources. Despite removing portions of GEO-optimized content, OFF-GEO also maintains competitive answer quality on desired queries