Learning objectives
- • Choose between prompt changes, RAG, supervised fine-tuning, and adapters
- • Evaluate fine-tunes for generalization, safety, and memorization
- • Manage adapter versioning, serving, and rollback
LLMOps
Fine-Tuning Operations and Adapter Management
Cover fine-tune vs RAG decisions, dataset curation, LoRA adapters, evaluation, rollout, and model governance.
Recommended on day 5790 minutesAdvanced
Interview prompts
- • When should you fine-tune instead of using RAG?
- • How do you evaluate whether a LoRA adapter is safe to promote?
Prerequisites