Retrieval-augmented generation (RAG) systems benefit from the integration of retrieval capabilities with large language models (LLMs), and one critical aspect of optimizing such systems is determining the ideal chunk size for data processing. The text discusses how the LlamaIndex Response Evaluation module can be used to identify the best chunk size by balancing the granularity of data retrieval with system efficiency. Smaller chunk sizes allow for more granular data retrieval but risk missing critical information, while larger sizes provide comprehensive context but may slow response times. The evaluation process involves using metrics such as Average Response Time, Faithfulness, and Relevancy across various chunk sizes to find an optimal balance. Experimental results suggest that a chunk size of 1024 offers a favorable compromise by maximizing faithfulness and relevancy without significantly impacting response time. The process underscores the importance of empirical testing in configuring RAG systems for optimal performance.