How I used Gemini CLI to orchestrate a complex RAG migration

What starts as a migration story quickly becomes a practical blueprint for using an AI CLI tool as a genuine project orchestrator rather than a fancy autocomplete. The author walked a real retrieval-augmented generation pipeline through multiple phases on Google Cloud, using Gemini CLI to coordinate the moving parts: schema changes, embedding model swaps, reindexing jobs, and validation checkpoints. The value is not in the specific cloud services chosen but in the pattern, letting the CLI hold context across steps, generate migration scripts, catch inconsistencies between phases, and reduce the mental overhead of tracking dependencies manually. For anyone rebuilding a retrieval layer, the honest insight here is that the hard part is sequencing, not coding individual pieces. The write-up shows how to delegate that sequencing to an AI tool in a way that is auditable and recoverable. The reservation is that the approach leans heavily on Google tooling, so founders running on other stacks will need to mentally translate quite a bit before the patterns feel portable. -> Best for: technical founders or solo engineers rebuilding or scaling a RAG system who want a structured orchestration approach rather than ad hoc prompt-and-pray migrations.