AI services
LLM fine-tuning & custom model training
General-purpose language models know a lot about the world and very little about your business. Fine-tuning trains a model on your own examples so it uses your terminology, follows your formats and handles your edge cases consistently.
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What's included
- LoRA/QLoRA fine-tuning
- Domain adaptation
- RLHF implementation
- Multi-modal training
What is LLM fine-tuning?
LLM fine-tuning is further training of a pre-trained large language model on your own examples, so it learns a specific task, format, tone or vocabulary. Techniques such as LoRA and QLoRA train a small set of extra parameters instead of the whole model, which keeps training faster and cheaper.
Where it helps
Common uses for LLM fine-tuning
We start by checking whether fine-tuning is the right tool. Better prompts or retrieval (RAG) often solve the problem for less. When they don't, we fine-tune and prove the improvement with a benchmark built from your data.
Industries
Domain language
Models that understand the vocabulary of legal, medical, financial or technical work in your field.
Reliable structured output
Extracting fields into a fixed format every time, for documents, emails or forms.
Brand voice and style
Drafts that follow your tone, templates and rules without long prompts.
Smaller, cheaper models
Teaching a smaller model one task so it runs faster and costs less, including on your own infrastructure.
Prompt engineering vs RAG vs fine-tuning
| Criteria | Prompt engineering | RAG | Fine-tuning |
|---|---|---|---|
| Best for | Quick improvements to instructions and format | Answers based on facts that change, such as docs and policies | Consistent behaviour, format, tone or vocabulary |
| How it uses your data | Examples written into the prompt | Relevant passages retrieved for each question | The model is trained on your examples |
| Keeping knowledge current | Limited to what fits in the prompt | Update the documents | Requires retraining |
| Shows its sources | No | Yes | No |
| Upfront effort | Low | Medium | Medium to high |
How we deliver
From first call to production
- 01
Define the target
We collect examples of good outputs and build an evaluation set that reflects your real cases.
- 02
Prepare the data
We clean, label and de-duplicate training data and remove sensitive information that should not be learned.
- 03
Train and compare
We fine-tune with LoRA, QLoRA or full training and compare the result against the base model and a prompting or RAG baseline.
- 04
Deploy and monitor
We serve the model through an API or in your cloud account, track quality and retrain as your data grows.
Technology we use
- OpenAI
- Anthropic Claude
- Llama
- Mistral
- Hugging Face
- PyTorch
- MLflow
- AWS
- Azure
FAQ
Questions about LLM fine-tuning
Do we need fine-tuning or RAG?
RAG is best when answers depend on facts that change, such as documentation or policies. Fine-tuning is best for teaching a consistent behaviour, format or vocabulary. Many systems use both, and we'll recommend one after looking at your use case.
How much training data do we need?
It depends on the task. A narrow task can improve with a few hundred good examples, while broader changes in behaviour need more. We assess your data during discovery.
Who owns the fine-tuned model?
You do. The training data, code and, where the model provider allows export, the model weights belong to you.
Can the model run on our own servers?
Yes, with open models such as Llama or Mistral. We can deploy them in your cloud account or on-premises so your data stays in your environment.
Free consultation
Talk to us about LLM fine-tuning
Book a free 60-minute strategy session. We'll look at your use case, check feasibility and recommend an approach.
No commitment required · NDA protected · Response within 24 hours
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