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Core
Catastrophic Forgetting and Continual Learning
Catastrophic forgetting is when training a neural network on new data erodes capabilities it already had, because gradient updates overwrite the weights that encoded old skills. Applied AI interviews probe it because fine-tuning a model on a narrow task is the most common way teams accidentally break a general model, and knowing the mitigations (data replay, regularization, parameter-efficient methods) separates people who have shipped fine-tunes from people who have only read about them.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsWhat is catastrophic forgetting, and how do you prevent it when fine-tuning or continually training an LLM?→LLM & GenAI FundamentalsExplain LoRA, QLoRA, and parameter-efficient fine-tuning. Why train a fraction of the parameters?→ML Infrastructure & GPUsHow do you serve many fine-tuned model variants efficiently (multi-LoRA serving)?→LLM & GenAI FundamentalsWhat is QLoRA, and how does it make fine-tuning large models feasible on one GPU?→LLM & GenAI FundamentalsWhen do you choose prompting vs RAG vs fine-tuning for a customer problem?→LLM & GenAI FundamentalsWalk through RLHF, then explain DPO and why it has largely displaced PPO-based RLHF.→
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