This paper investigates optimal practices for retrieval-augmented generation (RAG) to enhance the performance and efficiency of large language models (LLMs). RAG integrates external knowledge to improve accuracy, reduce hallucinations, and enhance response quality, particularly in specialized domains. However, existing RAG approaches face challenges such as complex implementation and slow response times. The study proposes a systematic approach to identify optimal RAG practices through extensive experiments, balancing performance and efficiency. The RAG workflow includes several modules: query classification, chunking, vector databases, retrieval methods, reranking, document repacking, summarization, and generator fine-tuning. Each module is evaluated for its impact on overall performance. The study finds that query classification improves accuracy and reduces latency, while retrieval and reranking significantly enhance the system's ability to handle diverse queries. Document repacking and summarization further refine the system's output, ensuring high-quality responses across different tasks. The study recommends two distinct RAG practices: one focused on maximizing performance and another balancing efficiency and effectiveness. The best performance practice includes query classification, "Hybrid with HyDE" retrieval, monoT5 reranking, reverse repacking, and Recomp summarization. The balanced efficiency practice includes query classification, Hybrid retrieval, TILDEv2 reranking, reverse repacking, and Recomp summarization. The study also extends RAG to multimodal applications, incorporating text-to-image and image-to-text retrieval capabilities. This extension improves the system's ability to handle visual inputs and accelerate the generation of multimodal content. The findings contribute to a deeper understanding of RAG systems and establish a foundation for future research. The study highlights the importance of modular design and the need for further exploration of chunking techniques and cross-modal retrieval methods.This paper investigates optimal practices for retrieval-augmented generation (RAG) to enhance the performance and efficiency of large language models (LLMs). RAG integrates external knowledge to improve accuracy, reduce hallucinations, and enhance response quality, particularly in specialized domains. However, existing RAG approaches face challenges such as complex implementation and slow response times. The study proposes a systematic approach to identify optimal RAG practices through extensive experiments, balancing performance and efficiency. The RAG workflow includes several modules: query classification, chunking, vector databases, retrieval methods, reranking, document repacking, summarization, and generator fine-tuning. Each module is evaluated for its impact on overall performance. The study finds that query classification improves accuracy and reduces latency, while retrieval and reranking significantly enhance the system's ability to handle diverse queries. Document repacking and summarization further refine the system's output, ensuring high-quality responses across different tasks. The study recommends two distinct RAG practices: one focused on maximizing performance and another balancing efficiency and effectiveness. The best performance practice includes query classification, "Hybrid with HyDE" retrieval, monoT5 reranking, reverse repacking, and Recomp summarization. The balanced efficiency practice includes query classification, Hybrid retrieval, TILDEv2 reranking, reverse repacking, and Recomp summarization. The study also extends RAG to multimodal applications, incorporating text-to-image and image-to-text retrieval capabilities. This extension improves the system's ability to handle visual inputs and accelerate the generation of multimodal content. The findings contribute to a deeper understanding of RAG systems and establish a foundation for future research. The study highlights the importance of modular design and the need for further exploration of chunking techniques and cross-modal retrieval methods.