# What is fine-tuning an AI model? Training an existing model a little further on your own examples so it picks up a task, a format or a style. Often a good prompt does the job first. - Page: https://tecyfy.com/concepts/fine-tuning - Subject: How AI works (https://tecyfy.com/concepts) - Level: intermediate - Also called: Fine Tuning, Finetuning - The lesson: How AI learns: a guessing game, not a database (https://tecyfy.com/learn/how-ai-works/how-ai-learns) - Last checked: 2026-10-14 Every assistant you use has already been fine-tuned. A model fresh from pretraining is good at continuing text, not at following instructions. It becomes ChatGPT or Claude through more training on examples of good answers. Fine-tuning is the same move made by a customer: take a finished model and train it a little more, on your own examples, so it behaves the way you need. ## What it is good for Fine-tuning teaches *how*, more than *what*. It works well for: - **A fixed format.** Always reply in the exact structure your system expects. - **A narrow task.** Sorting support tickets into your 12 categories. - **A house style.** The tone your brand writes in. - **A smaller model.** Teaching a cheap model one job well enough that you do not need an expensive one. OpenAI lists the benefits as handling more examples than fit in a prompt, needing shorter prompts and making a smaller, cheaper model viable. ## What it is not good for Keeping a model up to date with facts. Fine-tuning makes a model mimic the patterns in its examples, and a fact that changes next month would mean training again. For knowledge that changes, give the model the documents when it answers, with [RAG](https://tecyfy.com/concepts/rag) or search. ## Try the prompt first OpenAI tells developers to write tests first, because a better prompt may be all you need. A few good examples in the prompt and a clear [system prompt](https://tecyfy.com/concepts/system-prompt) are quicker to try, and cheaper to change. ## The ground is shifting Fine-tuning is getting harder to buy from the largest providers. Anthropic's API does not offer it. OpenAI announced in May 2026 that it is winding down its fine-tuning platform: new users were shut out at once, and active existing customers can no longer start new training jobs from 6 January 2027. Models already fine-tuned keep working until the model they were built on is retired. Open models are another matter. Whoever downloads one can train it further on their own hardware, and methods such as [LoRA](https://tecyfy.com/concepts#lora) exist to make that cheaper. ## Before you start Collect real examples of the input and the output you want. Write down how you will judge success. Then see how far a careful prompt gets you. If it falls short on format or consistency, not on facts, fine-tuning may be worth it. ## Related - [Large Language Model](https://tecyfy.com/concepts/large-language-model): A model trained on a huge amount of text to predict the next token. Further training turns it into an assistant that follows instructions. - [Supervised Fine‑Tuning](https://tecyfy.com/concepts#supervised-fine-tuning): Standard fine‑tuning on labeled input‑output pairs. - [LoRA](https://tecyfy.com/concepts#lora): Low‑Rank Adaptation, a parameter‑efficient fine‑tuning method - [Parameter‑Efficient Fine‑Tuning](https://tecyfy.com/concepts#parameter-efficient-fine-tuning): Adapting a model by updating only a small subset of parameters - [Reinforcement Learning from Human Feedback](https://tecyfy.com/concepts#reinforcement-learning-from-human-feedback): A training technique that aligns model outputs with human preferences - [RAG](https://tecyfy.com/concepts/rag): A way to answer from your own documents. A search finds the passages that match the question, and they are added to the prompt before the model writes. ## Sources - [Glossary](https://platform.claude.com/docs/en/about-claude/glossary), Anthropic, Claude API documentation, 2026-10-14. Fine-tuning is further training of a pretrained language model with additional data, so the model starts to mimic the patterns of the fine-tuning dataset. Claude has already been fine-tuned to be a helpful assistant. The Claude API does not currently offer fine-tuning. Fine-tuning can adapt a model to a domain, task or writing style, and needs careful thought about the data and its effect on performance and biases. - [Model optimization](https://developers.openai.com/api/docs/guides/model-optimization), OpenAI API documentation, 2026-10-14. Fine-tuning takes a base model and the kinds of inputs and outputs you expect and gives a model that excels at those tasks. It can handle more examples than fit in a prompt, allow shorter prompts and a smaller, cheaper model. Supervised fine-tuning trains on correct responses, DPO on correct and incorrect ones, reinforcement fine-tuning grades outputs, and vision fine-tuning uses images. Write evals first, because prompt engineering may be all you need. OpenAI is winding down the fine-tuning platform. It is no longer open to new users, existing users can create training jobs for the coming months, and fine-tuned models stay available until their base models are deprecated. - [Deprecations](https://developers.openai.com/api/docs/deprecations), OpenAI API documentation, 2026-10-14. Developers were notified on 7 May 2026. From 2 July 2026 organisations that had not run inference on a fine-tuned model in the past 60 days can no longer create fine-tuning jobs. From 6 January 2027 active existing customers can no longer create new fine-tuning jobs. Inference on fine-tuned models is disabled only when the underlying base model is deprecated. - [RAG (retrieval augmented generation), in the Glossary](https://platform.claude.com/docs/en/about-claude/glossary#rag-retrieval-augmented-generation), Anthropic, Claude API documentation, 2026-10-14. RAG passes documents into the context window at runtime, so the model can use information beyond its training data. It is particularly useful for tasks that need up-to-date information. - [How ChatGPT and our foundation models are developed](https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-foundation-models-are-developed), OpenAI Help Center, 2026-09-25. Models are developed in stages that include pre-training and post-training.