Quality matters in search, and ranking models are a low hanging fruit strategy to improve search quality. These models take the query and each candidate document as input to predict relevance. Cross Encoders, Metadata Rankers, and Score Rankers are three different genres of ranking models that can be used for content-based re-ranking, context-based re-ranking, and scoring or detecting content about candidate documents, respectively. These models offer more personalized and context-aware search experiences by further reasoning about the relevance of results without needing specialized training. Ranking is particularly exciting for retrieval-augmented generation as it can improve the quality of intermediate tasks in complex LLM-driven workflows.