A travel firm runs a customer assistant on a fluent general-purpose foundation model. Its fares and route timetables change several times a week, and every answer must reflect the live schedule rather than stale figures baked in at training time. The team is choosing between retrieval-augmented generation and fine-tuning. Which two factors most strongly favour retrieval-augmented generation over fine-tuning for this use case? Select TWO.
- AThe source data changes several times a week, so answers must reflect content that can be updated without retraining the model. Correct
- BThe assistant must adopt a distinctive brand voice and reply format that the base model does not produce on its own.
- CThe team wants the timetable knowledge stored permanently inside the model weights so no external store is queried at run time.
- DEach answer should be grounded in retrievable source passages the team can update independently of the model itself. Correct
- EThe base model already lacks fluency on travel topics and needs its core language ability rebuilt from scratch.
Why A is correct: RAG fetches current passages at query time, so refreshing the index keeps answers current, whereas fine-tuning would need a costly retrain on every schedule change.
Why B is wrong: Shaping tone and structure is a behaviour-adaptation goal better served by fine-tuning or prompt design, not by retrieving fresh documents.
Why C is wrong: Baking knowledge into weights describes fine-tuning, and it suits static facts, so it argues against RAG rather than for it.
Why D is correct: Grounding replies in an external, separately maintained document set is the defining benefit of RAG and decouples knowledge updates from the model.
Why E is wrong: The stem states the model is already fluent, so rebuilding language ability is unnecessary and would point to pre-training, not RAG.