Fine-Tuning LLMs with LoRA
Fine-tuning is useful when a model repeatedly exhibits the wrong behaviour and high-quality examples can define the behaviour you want. It is not the default way to add changing facts, private documents, or authoritative records to an application. That distinction matters. Training a model on policy documents may make its language sound familiar, but it does not provide reliable document versioning, access control, citations, or guaranteed recall. Retrieval-augmented generation (RAG) or a deterministic tool is usually a better interface to knowledge that must remain inspectable. ...