Implementing a RAG system: Run

In the "Crawl" and "Walk" phases, I introduced the basics of RAG and explored ways to optimize the pipeline to increase efficiency and accuracy. Armed with this knowledge, it's time to productionize our learnings. Run In the "Crawl" and "Walk" phases, we explored RAG fundamentals using local tools, proving how much document processing and re-ranking impact performance. While you could certainly scale those manual workflows into production, do you really wan to manage the infrastructure, data pipelines and scaling hurdles yourself? Welcome to the "Run" phase. Here we leverage Google Cloud's Vertex AI RAG Engine - a fully managed solution that automates the entire pipeline so you can focus on building, not maintenance. Vertex AI RAG Engine Vertex AI RAG Engine is a low-code, fully managed solution for building AI applications on private data. It handles the ingestion, document processing, embedding, retrieval, ranking, and grounding to ensure that the response is highly accurate and rele