Researchers explore the impact of chunking and representation on retrieval-augmented generation in chemistry-aware question answering. They evaluate 41 embedding models on a new benchmark and find that retrieval-tuned E5, BGE, and Nomic models perform well. They also identify optimal chunking strategies and sizes for practical application.