Your question is Approach LLM Fine-Tuning for Tasks. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You have a general-purpose language model, but your product needs more consistent behavior on a specific task such as customer support response drafting, dispute reason classification, or policy-grounded answer generation. The base model works reasonably well with prompting, but quality is uneven and you are considering fine-tuning.
Can you explain how you would approach fine-tuning a large language model for a specific task?