Controlling Output Rankings in LLM-Based Search via Generative Engine Optimization
CORE is an optimization method designed to control how large language models (LLMs) rank search results in generative engines. It achieves this by strategically appending three types of content—string, reasoning, and review-based—to retrieved items. Experiments show CORE significantly increases the success rate of promoting a target product across various LLMs and product categories.