# Fine-tuning

> Fine-tuning is the process of further training a pre-trained AI model on your own examples so that it performs a specific task, tone or output format better. It changes how a model answers; it is not a good way to keep what the model knows up to date. Most business projects try prompt engineering and RAG first.

URL: https://corrplus.net/glossary/fine-tuning/
Updated: 2026-10-22
Corrplus Team

**Definition:** Fine-tuning is the process of further training a pre-trained AI model on your own examples so that it performs a specific task, tone or output format better.

## How is fine-tuning done?

The model is shown many input-output examples, and its weights are updated to fit them. Providers such as OpenAI and Google offer fine-tuning for some of their models through an API. For open-weight models, parameter-efficient methods such as LoRA train only a small part of the model and keep costs down. After training, the model should be measured against the base model on a held-out test set.

## When do you need fine-tuning?

When you need a consistent tone or brand voice, when output must follow a strict format every time, or when you want a smaller, cheaper model to match a larger one on a narrow classification task. For current, changing knowledge, [RAG](/glossary/rag/) is faster and cheaper; for simple behaviour changes, [prompt engineering](/glossary/prompt-engineering/) usually suffices. The methods can be combined.

## Data and compliance

Industry jargon, abbreviations or an in-house writing style, in Turkish or any other language, are reasonable grounds for fine-tuning. Personal data in training sets should be anonymised. If training data is sent to a provider, review international transfers under GDPR and [KVKK](/glossary/kvkk/), Türkiye's data protection law, with your legal adviser. Training and running the model on your own infrastructure keeps the data in-house. For the underlying model types, see [LLM](/glossary/llm/).

## Frequently asked questions

### Should we fine-tune a model to teach it our company documents?

Usually not. The model would need retraining whenever documents change, and you could not trace which document an answer came from. For document-based question answering, RAG is the better fit.

### How much data does fine-tuning need?

It depends on the task and the method. For a classification or formatting task, a few hundred good examples can produce meaningful results; a small, consistent set beats a large, messy one. Start with a small trial.