RAG systems force a trade-off: small chunks embed well but lose context, while large chunks preserve meaning but reduce search accuracy. Parent Document Retriever solves this by separating what you search from what you return indexing small semantic chunks for precision, while retrieving larger parent documents for rich LLM context.
We will compare naive RAG with PDR, break down the dual-store architecture, discuss when it works (and when it fails), and build a working implementation using LangChain with persistent storage.