AI model fine-tuning
Fine-tuning is useful when a base model does not reach the format, tone, or accuracy you need. It requires quality data and a clear evaluation.
Is this the right service?
When it is a fit
- The model does not follow the required format.
- You need a specific tone or style.
- You have repetitive classification tasks.
- You have labeled data.
- RAG does not solve the problem.
When it is not
- There is no labeled data. Build the dataset first.
- RAG solves the case. RAG is simpler for documents. See RAG for businesses
- A prompt handles it. Training is not necessary.
- Information changes frequently. RAG updates with the documents. See RAG for businesses
- It is high risk without review. Human oversight is required.
What is included
- Use case and data review.
- Design and evaluation.
- Implementation and integration.
- Human oversight and documentation.
How it works
- Review the use case and data.
- Design and evaluate.
- Implement and integrate.
- Document and monitor.
Before we start
What we need
- Dataset.
- Goal.
- Evaluation.
- Responsible contact.
What is not included
- Licenses and usage.
- Massive labeling.
- 24/7 support.
Typical cases
Ticket classification
Labeled dataset and evaluation.
Field extraction
Fine-tuning for format.
Brand tone
Example dataset.
Frequently asked questions
When do I not need this service?
If there is no data, if RAG solves it, or if a simple prompt is enough, it does not apply. See the When it is not section.
Do I need labeled data?
Yes, supervised fine-tuning requires a representative dataset.
Who owns the model?
It depends on the provider and contract; we define it before starting.
Does it include model usage?
No. Usage is quoted separately.
How is it evaluated?
With a test set and agreed metrics.
Is it secure?
Confidentiality, access, and data use are agreed.
Not your case?
If rag solves the case., the recommended service is RAG for businesses.
See all services