💡 The paper introduces Adaptive Chunking, a framework that optimizes chunking method selection for Retrieval-Augmented Generation RAG by using intrinsic document metrics. The effectiveness of RAG depends on how documents are segmented into smaller units, but traditional one-size-fits-all approaches often fail to capture the nuances of diverse texts. To address this, the authors propose a framework that selects the most suitable chunking strategy for each document based on five novel metrics: References Completeness, Intrachunk Cohesion, Document Contextual Coherence, Block Integrity, and Size Compliance. These metrics assess chunking quality across key dimensions. The authors also introduce two new chunkers and targeted post-processing techniques to support the framework. The results show that the adaptive method significantly improves downstream RAG performance, increasing answer correctness to 72% and the number of successfully answered questions by over 30%, without changing models or prompts. The framework demonstrates that adaptive, document-aware chunking guided by intrinsic metrics offers a practical path to more robust RAG systems. The code for the framework is available, making it possible for others to implement and build upon the research. Overall, the paper contributes to the development of more effective RAG systems by providing a novel approach to chunking that takes into account the unique characteristics of each document.