Breaking up is hard to do: Chunking in RAG applications

Retrieval-augmented generation (RAG) is increasingly used to ground LLM responses in specific data sources. Proper data chunking is crucial for effective RAG systems, as chunk size and method significantly impact search results and response accuracy. Common strategies include fixed-size, random, context-aware, and adaptive chunking. While smaller, semantically coherent chunks often perform best, the optimal approach varies by use case. Testing different methods and evaluating results is essential for developing an effective RAG system.