The exploration of chunking methods for language-agnostic retrieval-augmented generation (RAG) systems reveals that chunking is indeed influenced by language, with significant variations observed across English, Hindi, French, and Spanish. The analysis compared several chunking methods, including fixed character splitting, recursive character splitting, semantic chunking, clustering, and LLM-based approaches, highlighting that the optimal chunking method and size depend on both the language and the use case. The study found that smaller chunk sizes generally improve retrieval precision but may lose context, while larger chunks offer more context but risk including irrelevant information. Semantic and clustering-based approaches generally outperform fixed character splitters by better preserving context. English and Hindi exhibited similar chunking behaviors, whereas French and Spanish required distinct approaches due to their complex morphology. The research emphasizes the need for language-aware preprocessing and suggests experimenting with different chunking methods and parameters, as well as considering the type of content and the retrieval process, to optimize the performance of multilingual RAG systems.