How to Answer
“Three levers, and they fix different failures. Prompting fixes instruction-following — the model can do the task, it just isn’t doing it your way. Retrieval fixes missing knowledge — the model doesn’t know your data, or your data changes daily. Fine-tuning fixes consistency — the model knows how, but drifts on format or tone across thousands of calls, or you need a smaller model to hit a latency and cost target.
The order matters: prompt, then retrieval, then fine-tune. Most teams reach for fine-tuning first because it sounds like the serious answer, spend six weeks building a dataset, and find out the real problem was a vague system prompt.
In production they combine. Fine-tune for shape, retrieve for facts. What I never do is fine-tune facts into a model, because then every fact change is a retraining job.”