Retrieval-Augmented Generation (RAG) pipelines enhance document retrieval by using a retriever for initial document fetching and rerankers for ordering based on semantic relevance to a query. Rerankers, employing models like transformers, refine search results by analyzing the interaction between query terms and document content, significantly improving retrieval quality. Deployment options for rerankers include as-a-Service APIs, cloud-hosted, and self-hosted solutions, each offering varying levels of control and integration flexibility. Open-source tools like ColBERT and FlashRank and commercial providers such as Cohere and Jina offer reranking capabilities. These tools employ advanced techniques like cross-attention mechanisms and support multilingual and complex data formats. Different encoder architectures, such as Bi-Encoders and Cross-Encoders, cater to varied needs, balancing performance and scalability. Large Language Models (LLMs) can also be used for reranking, offering more precise results at the cost of higher latency and expense. Implementations like LlamaInde and n8n's Reranker Cohere node facilitate the integration of reranking into existing workflows, ensuring that the most relevant information is prioritized in RAG systems.