The chunking strategy point hits hard. I've built RAG pipelines for client document processing and the number one issue is always naive chunking destroying context across sections. Fixed-size chunks with overlap sound fine in theory but fall apart with tables, multi-part instructions, or documents where context carries across pages. The biggest wins I've seen come from semantic chunking based on document structure plus a re-ranking step before the LLM sees the results. Also, evaluation is massively underinvested — most teams have no idea how their retrieval quality changes as the corpus grows.