Catastrophic Forgetting in Fine-Tuned LLMs: A Mitigation Playbook

1Key Takeaways

• 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….