Dave Jones explains Low-Rank Adaptation (LoRa) as a parameter-efficient way to fine-tune large language models without retraining the entire base. By adding a small "adapter" of matrices to the serving harness, the model's output can be nudged toward specific expertise, such as identifying slop. This method is more scalable than Retrieval-Augmented Generation (RAG) for high-customization tasks like spam detection.
