The JIT-LoRA system updates a running language model's LoRA weights during conversation.

The project describes just-in-time LoRA training as background backpropagation applied while the assistant remains active. Its stated design modifies adapter weights after each response rather than waiting for a separate offline fine-tuning run. This is a narrow form of online parameter adaptation: the base model remains fixed while low-rank updates alter its behaviour. The distinction matters because the system is not merely retrieving text from a memory store; it is changing trainable parameters during interaction.