Retrieval-Augmented Generation (RAG) systems are powerful—until your infrastructure needs to evolve. What starts as a functional pipeline can quickly become difficult to manage when: Vector databases need replacing Embedding models change Retrieval strategies evolve Document schemas expand Prompt chains become fragmented Migrating a production-grade RAG system is not just a data transfer problem. It’s an orchestration problem. Recently, I used Gemini CLI to help manage and accelerate a complex RAG migration involving: Embedding model upgrades Vector store restructuring Metadata normalization Prompt workflow rewrites Validation across multiple retrieval layers This article breaks down how Gemini CLI became a practical operational layer for planning, execution, and verification. The Initial Problem Our legacy RAG stack had grown messy.…