Question

A financial services team is building a customer support agent in Azure AI Foundry. The agent must answer questions using a knowledge base of 40,000 policy documents that are updated weekly, and it must consistently adopt the company's formal brand tone and a specific structured response format. The team wants to minimize ongoing maintenance overhead while keeping answers accurate against the latest documents. Which approach should they adopt?

Accepted Answer

Use retrieval-augmented generation (RAG) over an indexed vector store for the documents, and shape tone and format through the system prompt and few-shot examples.

Answer

Fine-tune a base model on both the 40,000 documents and the brand tone examples, then redeploy the fine-tuned model each week.

Answer

Fine-tune the model on the brand tone and format examples, and load all 40,000 documents into the system prompt as context on every request.

Answer

Fine-tune the model weekly on the tone examples and rely on the model's pretrained general knowledge to answer policy questions.