A Generative AI Engineer is building a RAG application for answering employee questions on company policies.
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.
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
C.
Ingest documents from a source -> Index the documents and save to Vector Search -> Evaluate model -> Deploy it using Model Serving -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response
D.
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
Evaluation must happen after generation to assess output quality (e.g., hallucination rate, grounding accuracy, etc.). This sequencing is critical in production pipelines where model safety and performance matter.
The answer marked as should not be considered valid - You can’t evaluate what hasn’t yet been generated.
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Lg22
4 weeks agocsrazdan
1 month agoDavidMiller
1 month, 1 week ago