為企業構建 RAG 管道：從 Kiro IDE 到 AWS 上的知識庫

I have just completed a simple but effective RAG (Retrieval-Augmented Generation) architecture, integrated directly into the IDE to speed up internal knowledge search tasks.

Why is this architecture suitable for Enterprise?

✅ Sensitive data doesn't leave the AWS Cloud — in line with the industry's stringent data control and security requirements.
✅ Clear separation of Local Environment and AWS Cloud, easy to audit, easy to scale
✅ MCP Server acts as a bridge to standardize, allowing many different "knowledge tools" (not just RAG) to be plugged in without having to modify the IDE
✅ Practical applications: look up system architecture documents, business processes, compliance policies, operational runbooks — Engineers ask directly in the IDE instead of searching documents.

This is a small piece of the journey of building an Engineering Intelligence Platform (EIP) that my team is implementing, where AI agents become real assistants for engineers, not just chatbots.

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