Tuning is overkill when the need is experimental or low-volume, when requirements change frequently, or when no quality dataset exists. This scenario fails on all three counts: it is a short, low-volume proof of concept, the requirements shift weekly, and the team has only a few dozen rough examples instead of the few hundred to few thousand curated pairs that supervised fine-tuning requires. The right move is to stay on prompting and grounding, which adapt instantly and cost far less.

Plain-Language Explanation:

You would not commission an expensive tailored suit for a body shape that keeps changing and a wardrobe you rarely wear. You buy off-the-rack, which is cheap, instant, and easily swapped. An experimental, fast-moving, low-volume project is exactly that situation, so a good prompt beats a costly, quickly outdated tuned model.

Option Analysis:852

A treats tuning as the first corrective step, but tuning is the last rung and jumping to it on any underperformance is the classic trap. B claims a few dozen rough examples suffice, yet supervised fine-tuning needs a few hundred to a few thousand curated, high-quality pairs. D rests the decision on duration alone, but volume, requirement stability, and dataset quality all still matter, so a long but low-volume, fast-changing project still should not be tuned.