While achieving a basic Retrieval Augmented Generation (RAG) is relatively straightforward, attaining superior results requires tuning and optimizing various factors, such as a careful selection of embedding models. Additionally, applying advanced techniques, such as multi-stage retrieval with rerankers, is essential. A methodology for quality evaluation is also critical to success in crafting the best strategy for your specific use case. This talk will introduce the landscape of available optimization techniques and provide advice on best practices.