Fine-tuning is the process of taking a pre-trained AI model and training it further on a smaller set of your own examples, so it behaves in a more specific way. A fine-tuned model might follow a brand's tone of voice, use a fixed output format or classify support tickets more reliably than the general model.
For businesses, fine-tuning is one of several ways to customize AI, alongside better prompts and connecting a model to your own documents. It's useful to understand what it can and can't do, especially because it doesn't change how public tools like ChatGPT or Gemini talk about your brand to everyone else.
What is fine-tuning?
A large language model is first trained on a huge, general body of text. Fine-tuning adds a second, much smaller round of training using examples you supply, such as pairs of prompts and ideal answers. The result is a customized copy of the model, available only to the organization that trained it.
OpenAI's supervised fine-tuning guide describes it as training a model on example inputs and known-good outputs so it more reliably produces your desired style and content. OpenAI says the minimum is 10 examples, and that improvements typically appear with 50 to 100.
Why fine-tuning matters
It can make outputs more consistent. OpenAI's model optimization guide says fine-tuning can help models consistently format responses in a certain way or handle novel inputs.
It can reduce prompt length and cost. Because the model has learned the pattern, you may need fewer instructions and examples in each request. OpenAI notes this can save on token costs at scale.
It's not how brands get into public AI answers. Fine-tuning creates a private model. It doesn't teach ChatGPT, Gemini or Claude anything new about your company for other users. Public AI visibility depends on what those systems can find and trust on the web.
The options are changing. On May 7, 2026, OpenAI told developers it was winding down self-serve fine-tuning. Its deprecations page says new organizations can no longer create training jobs, and active existing customers lose that ability on January 6, 2027. Already fine-tuned models stay available for inference until their base models are deprecated. Other providers still offer it. Google Cloud, for example, documents supervised fine-tuning for Gemini models.
How fine-tuning works
Supervised fine-tuning
The most common approach. You provide examples of prompts and the exact responses you want, and the model learns to imitate them. OpenAI lists classification, translation and formatting as good fits.
Preference tuning
Methods like direct preference optimization (DPO) give the model pairs of better and worse responses, so it learns which style you prefer. OpenAI suggests this for tasks like summarization and tone.
Reinforcement fine-tuning
Here, responses are graded, and the model is reinforced toward higher-scoring answers. OpenAI notes this requires expert graders and applies to reasoning models.
Evaluation
Fine-tuning only helps if you can measure the result. OpenAI recommends holding back some examples and setting up evaluations before training, so you can compare the tuned model with the original.
When to consider fine-tuning
- Start with prompting. OpenAI says prompt engineering "may be all you need" and suggests fine-tuning only after trying prompts and examples. Good prompt engineering solves most marketing use cases.
- Use retrieval for facts. If the model needs current or specific information, such as product specs or policies, supplying it at answer time through retrieval-augmented generation is usually easier to keep up to date than retraining.
- Fine-tune for repeatable patterns. Consider it when you have a high-volume task with a stable format and hundreds of good examples, such as tagging content or producing structured product descriptions.
- Check provider availability. Confirm which models support fine-tuning and how long they'll be supported before you invest.
- Plan for upkeep. When base models are retired, tuned versions may need to be rebuilt on newer models.
Common mistakes and misconceptions
"Fine-tuning will make ChatGPT recommend us." It won't. A fine-tuned model is private to you. How public assistants describe your brand depends on their training data and what they retrieve from the web.
"Fine-tuning is the best way to add knowledge." OpenAI frames supervised fine-tuning around behavior, style and formatting. For facts that change, retrieval is usually a better fit, because you can update the source without retraining.
"More data is always better." Quality matters more than volume. A small set of accurate, consistent examples beats a large set of messy ones.
"It's a one-time project." Models get deprecated and replaced. A tuned model may need retraining, and evaluations should be rerun when anything changes.
Fine-tuning vs. RAG vs. prompt engineering
Prompt engineering changes the instructions you send, and it's the fastest to try. Retrieval-augmented generation gives the model relevant documents at answer time, which suits facts that change. Fine-tuning changes the model's behavior through training, which suits stable, repeated tasks at scale. Many teams combine them: a well-written prompt, retrieved context and, only where it pays off, a tuned model.
How Butter approaches fine-tuning
Butter doesn't offer fine-tuning or custom model development. There's also no service that puts a brand directly into a public AI model's training data, and we don't claim to offer one. What we do is improve the public footprint that models learn from and that AI search retrieves.
In our GEO plan, that means one AI-crawlable content citation and backlink each month, knowledge graph submissions and schema markup guidance, plus monthly prompt testing to see how AI tools describe you. We also walk you through the trade-offs of allowing or blocking training crawlers, so it's a deliberate choice. You can see how this fits on our GEO service page.
Frequently asked questions
Can I fine-tune ChatGPT to know about my business?
You can't change the public ChatGPT that other people use. Fine-tuning, where available, creates a private model for your own applications. Public visibility depends on what AI tools find and trust on the web.
How much data do I need to fine-tune a model?
OpenAI's minimum is 10 examples, and it says improvements typically appear with 50 to 100 well-crafted examples. Complex tasks may need more. Quality and consistency matter more than volume.
Is OpenAI still offering fine-tuning?
OpenAI is winding down self-serve fine-tuning. It closed training to new organizations in 2026, and active existing customers can't create new jobs after January 6, 2027. Existing fine-tuned models remain usable until their base models are deprecated.
Should a marketing team fine-tune a model?
Usually not as a first step. Clear prompts, good examples and connecting the model to your own documents cover most marketing needs. Fine-tuning makes sense for high-volume, repeatable tasks with plenty of good examples.