Part of AI Fine-Tuning
LLM fine-tuning
Fine-tune GPT, Llama, Mistral or Qwen models on your examples for accuracy, consistency and lower cost per call.
In one paragraph
LLM fine-tuning: Fine-tune GPT, Llama, Mistral or Qwen models on your examples for accuracy, consistency and lower cost per call. AI fine-tuning and custom model services adapt foundation models to your domain, tasks and output formats: instruction tuning, LoRA, RLHF-style preference tuning, custom embeddings and vision, audio or multimodal fine-tuning. It suits teams whose prompts have hit a quality or cost ceiling and who have, or can build, the examples needed to teach a model to do the job reliably.
How llm fine-tuning fits into ai fine-tuning
Consistent structured output
A tuned model returns your schema reliably, cutting parsing failures and retries.
Domain accuracy
Better performance on medical, legal, financial or technical language than generic prompting.
Cheaper, faster inference
Replace a large model plus long prompt with a small tuned model for high-volume tasks.
Brand voice and tone
Writing that sounds like you without a page of instructions on every call.
How we deliver
- 1
Baseline and case for tuning
Measure the best prompt-only approach first; fine-tune only if the gap in quality or cost justifies it.
- 2
Dataset build
Curate, label and clean examples, often LLM-assisted, with a held-out evaluation split.
- 3
Training runs
LoRA, full fine-tuning or preference tuning with experiment tracking and reproducible configs.
- 4
Evaluation
Compare against the baseline on the held-out set and on human review; check for regressions.
- 5
Deploy and refresh
Serve the model, monitor quality, and retrain as data and requirements evolve.
Frequently asked
Fine-tune when prompting has plateaued on quality, when you need consistent format or tone at high volume, or when a smaller model must do a job cheaply. Use RAG for knowledge that changes. Many production systems combine RAG for facts with a tuned model for behaviour. We always measure the prompt-only baseline first.
For behaviour and format, a few hundred high-quality examples often produce clear gains; for domain adaptation, thousands or more. Quality and coverage matter more than raw volume. If you lack examples we help generate and review them with LLM assistance.
Yes, where the provider offers it: OpenAI and several others support fine-tuning through their APIs, and Bedrock and Vertex offer options for their catalogues. For full control over weights and hosting we tune open models such as Llama, Mistral or Qwen.
For open models, you own the weights and adapters outright and can host them anywhere. For hosted-provider fine-tunes, the model lives in your provider account under their terms. Training data and evaluation sets are always yours. We hand over training scripts, configs and evaluation results with the weights so the model can be reproduced or retrained without us.
Ready to chart your AI course?
A free 30-minute consultation: we listen, ask hard questions and tell you honestly where AI will and won't help.