You are building a Retrieval-Augmented Generation (RAG) system that uses an LLM plus a vector database. Before creating embeddings and indexing documents, you must **split long documents into chunks**. Describe how you would design the **chunking strategy**. In your answer, discuss: - How you would choose **chunk size** and **overlap** and the trade-offs involved (recall vs. context size, latency, etc.). - How you would use **document structure** (e.g., headings, paragraphs, sections) vs. naive fixed-length splits. - When you might use more advanced methods like **semantic chunking** or dynamic chunk sizes. - How you would evaluate and iterate on your chunking strategy in a real system.