A product team runs a customer assistant on a fluent general-purpose foundation model and is deciding whether to invest in fine-tuning. Prompt engineering and retrieval are already on the table as cheaper alternatives. Which two situations most strongly justify the added data and compute cost of fine-tuning rather than relying on prompting or retrieval alone? Select TWO.
- AThe reference facts the assistant cites change several times each week and must always be current.
- BA few thousand labelled in-house examples of the desired behaviour exist that can be used to adapt the model. Correct
- CThe team simply wants to feed a few worked examples into the prompt to steer answers at request time.
- DThe assistant must consistently adopt a specialised output style that prompting alone fails to hold across varied inputs. Correct
- EThe model needs occasional access to a single internal document that a person could paste into the request.
Why A is wrong: Frequently changing facts favour retrieval, since fine-tuning bakes knowledge into weights that go stale the moment the data changes.
Why B is correct: Having a curated labelled dataset of the target behaviour is what makes fine-tuning feasible and worthwhile over prompting.
Why C is wrong: Supplying examples in the prompt is few-shot prompting, a lighter approach that avoids fine-tuning's data and compute cost entirely.
Why D is correct: A durable behaviour or style the base model will not reliably reproduce from instructions is a classic case where fine-tuning earns its cost.
Why E is wrong: Pasting one document into context is trivially handled by prompting or retrieval, so it gives no reason to fine-tune anything.