Workflow · workflow
Hackathon speedrun: build and deploy a RAG app in minutes with Vertex AI Studio and Vertex AI Search
Hackathon participants need to quickly build and deploy a grounded LLM Q&A application from a knowledge base without complex setups.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Upload docs to Cloud Storage
Source documents are uploaded to a Google Cloud Storage bucket as the knowledge base.
Tools used
Google Cloud StorageVertex AI SearchVertex AI StudioCloud Rungemini-2.0-flash-001
Outcome
A fully deployed, interactive RAG application was built from raw documents with minimal coding using Google Cloud's Vertex AI Studio and Vertex AI Search.
Grounding & classification
Source type: technical build writeup
17 fields verified against source quotes.
conversational aiknowledge searchragknowledge basesource backedtools describedworkflow describedtime savedtechnical build writeuprag answering