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You need to design a generative Al solution that uses a Microsoft SOL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
* Ait' grounded In the latest transactional and reference data stored in D61
* Do NOT require retraining or fine-tuning the language model when the data changes
* Can include citations or references to the source data used in the response
Which scenario is the best use case for implementing a Retrieval Augmented Generation (RAG) pattern? More than one answer choice may achieve the goal. Select the BEST answer
The best use case for RAG is answering user questions based on company-specific knowledge. Microsoft defines RAG as a pattern that augments a language model with a retrieval system that provides grounding data at inference time, which is exactly what you need when responses must be based on the latest transactional and reference data, must avoid retraining/fine-tuning, and should be able to include citations or references to source data.
The other options do not fit as well:
summarizing free-form user input does not inherently require retrieval from DB1,
training a custom model contradicts the requirement to avoid retraining/fine-tuning,
generating marketing slogans is a creative generation task, not a grounding-and-citation scenario. RAG is specifically strong when answers must come from your organization's own changing knowledge.