LLM Fine-tuning
Explains what fine-tuning actually changes in a model's behavior and how it compares with prompt optimization and RAG on cost and effect, helping teams decide whether training is worth it.
LLM fine-tuning uses extra training to make a model more stable in tone, output structure and specific tasks, with full fine-tuning, LoRA and QLoRA as common paths. LoRA trains only small side matrices, cutting memory and cost, and suits learning industry terms, fixed phrasing or image styles; but if the base model lacks a capability and data is thin or messy, fine-tuning won't rescue it.
