Machine Learning System Design Interview #48 - The Low-Rank Capacity Trap

Why using standard LoRA for domain adaptation starves your model of new concepts, and how to rewire your adapter strategy to bridge the performance gap with full fine-tuning.

You’re in a Senior LLM Engineer interview at Meta and the interviewer asks:

“Your team used a standard LoRA for continual pre-training on a massive, complex new domain to save vRAM. But the model’s performance is severely inferior to a full fine-tune. How do you modify the adapter architecture and optimization strategy to bridge this gap?”

Most candidates…