Your question is Fine-Tuning LLMs for Production. 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).
How do you handle fine-tuning large language models for production-level tasks?
Discuss the complete practical workflow, including dataset preparation, prompt and response formatting, parameter-efficient fine-tuning, validation, safety testing, deployment, monitoring, rollback, and retraining. Explain how you would choose between prompting, retrieval-augmented generation, supervised fine-tuning, and preference optimization under quality, latency, privacy, and budget constraints.
