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You are building a system where a language model must answer questions from a private document collection instead of relying only on its pretraining. The documents are split across policies, product notes, and support articles, so exact keyword matching is not enough. You want to use a vector database to retrieve the most relevant passages before generation.
How do you design a vector database-backed retrieval system for LLM applications?
