Your question is PEFT vs Full Fine-Tuning Trade-offs. 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).
Explain the trade-offs between parameter-efficient fine-tuning (PEFT/LoRA) and full-parameter fine-tuning when adapting an open-source model like Llama for a highly structured task at Decagon.
Discuss model quality, data requirements, optimization stability, memory and training cost, serving complexity, adapter composition, catastrophic forgetting, and operational maintenance. Define an evaluation plan for structured outputs, including exact-match correctness, schema validity, and invalid-output rate. Include a practical recommendation and explain when you would revisit that decision.