LLM Fine-Tuning & Customization

Fine-tuning a model is cheaper than it used to be. Not cheap. The discipline is taking pre-trained foundation models and pushing them toward specialized behavior, domain expertise, or stylistic consistency that generic models can't reach. Core techniques: supervised fine-tuning, RLHF and DPO alignment, LoRA and QLoRA for parameter-efficient training. The hidden 80% of the job is dataset curation, quality control, and the evaluation frameworks that tell you whether your tuned model is actually better. The judgment call is knowing when fine-tuning is the right approach at all, versus reaching for RAG or prompt engineering instead, and how to avoid catastrophic forgetting along the way.