What is catastrophic forgetting, and how do you prevent it when fine-tuning or continually training an LLM?
Fine-tune a model on your domain and it may lose the ability to do everything else. What earns the signal is explaining why shared weights cause it and listing the concrete mitigations that all boil down to one principle.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Fine-tune a model on your domain and it may lose the ability to do everything else. What earns the signal is explaining why shared weights cause it and listing the concrete mitigations that all boil down to one principle.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
