Answer-first summary for fast verification
Answer: 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
Option B is correct because it follows the proper sequence for building and deploying a RAG application: first, ingest and index documents into Vector Search to create the knowledge base; then, during usage, the LLM retrieves relevant documents and generates responses; finally, the model is evaluated and deployed. This aligns with the community consensus (100% selected B) and best practices where evaluation occurs after response generation to assess performance. Option A incorrectly places evaluation before response generation, Option C omits the query and response steps entirely, and Option D starts with user queries before building the knowledge base, which is illogical.
Author: LeetQuiz Editorial Team
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A Generative AI Engineer is building a RAG application to answer user questions about technical regulations while learning a new sport. What are the necessary steps to develop and deploy this RAG application?
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