When I started designing the RAG architecture for the internal AI assistant project, one of the first decisions I had to...

When I started designing the RAG architecture for the internal AI assistant project, one of the first decisions I had to make was:Where should the application's knowledge live, and which component should be responsible for turning that knowledge into something the model can retrieve?There are several ways to build a RAG system on AWS.I could assemble and manage the retrieval components myself, use a vector database directly, or use Amazon Bedrock Knowledge Bases to manage much of the RAG data pipeline.During the project, I explored the architecture from both perspectives before settling on an Amazon Bedrock Managed Knowledge Base.This article explains that decision and the technical tradeoffs behind it. The Initial ProblemThe application needed to answer questions using internal company documents.The source documents included:Brand GuidelinesScript BankCaption BankClient Proposal TemplateMy initial mental model was:Documents ↓Amazon S3 ↓Vector Storage ↓Similarity Search ↓Foundation Mod...