# What Is Fine-Tuning an AI Model? Definition | Gr8r | Gr8r Studio

> Fine-tuning is further training an AI model on your own examples to shape its behaviour or style. When it helps, when RAG or prompting is better, and the risks.

Source: https://gr8rstudio.com/glossary/fine-tuning

Glossary · AI

# Fine-tuning

Fine-tuning is further training an existing AI model on a set of your own examples so it learns a specific style, format or task more reliably than prompting alone can achieve.

[Plan your AI build](https://gr8rstudio.com/services/product-design)[All terms](https://gr8rstudio.com/glossary)

In one line

Fine-tuning is further training an existing AI model on a set of your own examples so it learns a specific style, format or task more reliably than prompting alone can achieve.

Related

- [LLM](https://gr8rstudio.com/glossary/large-language-model)
- [Prompt engineering](https://gr8rstudio.com/glossary/prompt-engineering)
- [RAG](https://gr8rstudio.com/glossary/retrieval-augmented-generation)

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On this page

1. [Why fine-tuning matters](#s-0)
2. [How fine-tuning works](#s-1)
3. [Common fine-tuning mistakes](#s-2)
4. [How Gr8r approaches fine-tuning](#s-3)

## Why fine-tuning matters

Some tasks need very consistent output, a particular tone or a narrow skill. Fine-tuning can make a model better at these, and sometimes lets a smaller, cheaper model do the job.

## How fine-tuning works

You prepare hundreds or thousands of high-quality input and output pairs. The model is trained further on them, producing a new version that leans towards those patterns.

The new model is then evaluated against the original to check it actually improved.

## Common fine-tuning mistakes

Fine-tuning to add knowledge that changes often, which RAG handles better. Training on messy or inconsistent examples. Skipping evaluation, so nobody knows if the tuned model is better.

## How Gr8r approaches fine-tuning

We usually try prompting and retrieval first because they are faster to change. We recommend fine-tuning only when evaluation shows a clear gap it can close, and we keep the training data and versions documented.

Example

A company fine-tunes a small model on several thousand approved product descriptions so new listings follow its house style and field structure without a long prompt each time.

FAQ

## Questions

Ask anything else

Reading our answers…

- Should I fine-tune or use RAG?
Use RAG to give the model facts that change. Use fine-tuning to shape behaviour, format or style. Some products use both.
- Do I own a fine-tuned model?
It depends on the provider's terms. Check who controls the weights, the training data and whether the model can be exported.

## Related terms.

- [ 01 LLMA large language model (LLM) is an AI model trained on very large amounts of text to predict and generate language. It can draft, summarise, classify, extract and reason over text, but it can also produce confident errors. Explore ](https://gr8rstudio.com/glossary/large-language-model)
- [ 02 Prompt engineeringPrompt engineering is the practice of writing, structuring and testing the instructions, context and examples given to a language model so it produces useful, consistent output for a specific task. Explore ](https://gr8rstudio.com/glossary/prompt-engineering)
- [ 03 RAGRetrieval-augmented generation (RAG) is a technique where an AI system first searches your own documents or data for relevant passages, then gives those passages to a language model so its answer is grounded in your information. Explore ](https://gr8rstudio.com/glossary/retrieval-augmented-generation)

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