Originally published on AI Tech Connect.
What you need to know Catastrophic forgetting is the tendency of a neural network to lose previously learned capabilities when it is trained on new data — and it is the single most common way a narrow-domain fine-tuning project quietly damages a production model. A support-ticket classifier that gets very good at your product taxonomy but starts failing basic instruction-following. A regional-language assistant that picks up fluent Tamil but loses its grip on multi-step reasoning in English. A compliance-document Q&A model that nails FCA terminology but can no longer hold a normal conversation. All three are catastrophic forgetting, and all three are avoidable. This playbook covers the three mitigation families that actually get used in production: parameter-efficient fine-tuning (PEFT)…
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