Fine-Tuning
Also known as: Supervised fine-tuning, SFT, LoRA fine-tuning
Continuing to train a pre-trained model on your own data to specialise its behaviour, tone or domain knowledge.
Fine-tuning takes a pre-trained model and trains it further on a smaller, curated dataset. The most common form is supervised fine-tuning (SFT), where the model is shown input/output pairs and learns to imitate them. Reinforcement learning approaches (RLHF, DPO, RLAIF) further shape model behaviour using preference data.
In 2026, full fine-tuning of frontier models is rare — most teams use parameter-efficient methods like LoRA, which trains a small adapter on top of frozen weights, or prompt RAG instead. Fine-tuning is the right tool when you need a consistent format, a specific tone, or a narrow skill (classification, extraction) at lower latency and cost.
Hosted fine-tuning is available from OpenAI, Anthropic, Google and most open-weight providers (Together, Fireworks, Replicate) for popular models like Llama and Mistral.
Related terms
- Large Language Model (LLM)
A neural network trained on massive text corpora to predict the next token, used for chat, coding, reasoning and as the brain inside AI agents.
- Retrieval-Augmented Generation (RAG)
A technique that retrieves relevant documents at query time and feeds them into the LLM's prompt, so the model can answer from your data instead of memorising it.
- Tokens
The atomic units that LLMs read and write — sub-word pieces produced by a tokenizer. Pricing and context limits are measured in tokens, not words.
More to explore
Other wiki entries that touch on Fine-Tuning.
- Prompt Engineering
The practice of designing inputs to LLMs to reliably produce useful outputs — through structure, examples, role-setting and constraints.
- Model Context Protocol (MCP)
An open standard from Anthropic for connecting LLMs to external tools and data sources through a uniform server interface.
- Perceptron
The first algorithmically trained neural network, invented by Frank Rosenblatt in 1957, and the foundational unit that sparked the deep-learning revolution.