Retrieval-Augmented Generation (RAG) systems can be significantly influenced by the choice of chunk size, but optimizing this parameter has traditionally been challenging due to the time and computational expense involved in reindexing datasets. LlamaCloud addresses these challenges by offering features such as index cloning, chunk visualization, and efficient iteration, which streamline the process of experimenting with different chunk sizes in RAG pipelines. By enabling developers to quickly create index copies with various chunking setups, inspect how documents are chunked, and iterate without complex manual management, LlamaCloud facilitates more effective chunk size optimization. This approach is demonstrated in an example workflow where developers can test questions with known answers to evaluate and refine their pipeline configurations. Further refinement of RAG systems can be achieved through systematic evaluation, automated testing, and domain-specific optimization, all of which can be supported by LlamaCloud's capabilities and its integration with observability and evaluation tools.