AI coding glossary
Fine-Tuning
Also known as: model fine-tuning, supervised fine-tuning, sft
In one sentence
Training a base LLM further on a smaller domain-specific dataset to specialize its behavior, distinct from prompt engineering and RAG.
Full definition
Fine-tuning updates the actual weights of a base LLM by training it further on a curated domain-specific dataset. It's the right tool when you need consistent behavior on a narrow domain that prompts and RAG can't reach, proprietary code patterns, internal terminology, specific output formats. In 2026 the practical mix is RAG + prompt engineering for most use cases (cheap, no model hosting), with fine-tuning reserved for high-volume, narrow tasks where the inference cost saved by a smaller fine-tuned model offsets the training + hosting cost. Modern options: OpenAI's fine-tuning API, Anthropic's custom models, open-source via Axolotl / unsloth on Llama / Mistral / Qwen bases, and parameter-efficient methods like LoRA for fast cheap fine-tunes.