Chonky is a novel neural network model designed for text semantic chunking, a crucial task in natural language processing (NLP). It aims to improve the way text is segmented into meaningful, coherent chunks, enhancing comprehension and context understanding in NLP applications. The model introduces advanced techniques that leverage deep learning to achieve better performance compared to traditional chunking methods. However, the evaluation of Chonky against the RAG (retrieval-augmented generation) benchmark is a pertinent consideration for corroborating its effectiveness. This indicates that while the initial results may be promising, broader evaluations against established benchmarks are essential for validation and comparison with existing models in the field.