A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport. What are the steps needed to build this RAG application and deploy it?
A.
Ingest documents from a source –> Index the documents and saves to Vector Search –> User submits queries against an LLM –> LLM retrieves relevant documents –> Evaluate model –> LLM generates a response –> Deploy it using Model Serving
B.
Ingest documents from a source –> Index the documents and save to Vector Search –> User submits queries against an LLM –> LLM retrieves relevant documents –> LLM generates a response -> Evaluate model –> Deploy it using Model Serving
C.
Ingest documents from a source –> Index the documents and save to Vector Search –> Evaluate model –> Deploy it using Model Serving
D.
User submits queries against an LLM –> Ingest documents from a source –> Index the documents and save to Vector Search –> LLM retrieves relevant documents –> LLM generates a response –> Evaluate model –> Deploy it using Model Serving
First, the RAG application including the LLM has to be built, and then you can evaluate and deploy, so B.
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