Model Fine-Tuning
Make AI models work better for your domain. We fine-tune language models on your data to improve accuracy, consistency, and cost efficiency.
Teach the model your domain
General-purpose models are good at everything but expert at nothing. Fine-tuning trains a model on your specific data so it understands your terminology, tone, and requirements.
Improved accuracy
Fine-tuned models understand your domain-specific terminology, formats, and expectations. Fewer errors, fewer hallucinations, better outputs.
Consistent tone
Train the model to match your brand voice, writing style, and communication standards. Consistent output without complex prompt engineering.
Lower costs
Fine-tuned smaller models can outperform larger general models for your specific tasks. Smaller models mean lower inference costs at scale.
Data quality drives results
Fine-tuning is only as good as the training data. We spend significant effort on data preparation, quality assurance, and evaluation before and after training.
Data preparation
We help you assemble, clean, and format training data. We identify gaps and ensure the dataset represents the full range of scenarios.
Evaluation framework
We define evaluation criteria and test sets before training begins. You know exactly how to measure whether fine-tuning improved performance.
Iterative refinement
Fine-tuning is rarely one-and-done. We train, evaluate, adjust the data, and retrain until the model meets your quality thresholds.
When fine-tuning makes sense
Specialist terminology
Your domain uses specific jargon, abbreviations, or formats that general models handle poorly. Fine-tuning teaches the model your language.
Structured outputs
You need the model to produce outputs in a specific format consistently. Fine-tuning is more reliable than prompt engineering for format compliance.
Cost reduction
You are using a large, expensive model and want to achieve similar quality with a smaller, cheaper one. Fine-tuning a smaller model for your task can dramatically reduce costs.
How we fine-tune models
Fine-tuning projects typically take four to eight weeks including data preparation, training, evaluation, and deployment.
Data and evaluation
We prepare training data, define evaluation criteria, and establish baseline performance. The quality of this step determines the quality of results.
Training and testing
We run fine-tuning jobs, evaluate against test sets, and iterate on data quality and training parameters until results meet your criteria.
Deploy and monitor
We deploy the fine-tuned model, set up monitoring for quality drift, and establish a process for retraining as your data evolves.
Frequently Asked Questions
How is fine-tuning different from RAG?
Fine-tuning changes how the model behaves: its style, format, and domain understanding. RAG changes what the model knows by providing documents at query time. They solve different problems and can be used together.
How much training data do we need?
It depends on the task. Some improvements require hundreds of examples; others need thousands. We assess your data and recommend the minimum viable dataset.
Does fine-tuning work with all models?
Most major model providers support fine-tuning for their smaller models. We recommend the best model and approach based on your requirements and budget.
Will we need to retrain regularly?
It depends on how quickly your domain changes. We set up monitoring to detect when the model's performance drops and retraining is needed.
Ready to improve your model's performance?
Fine-tuning starts with your data. Book a call and we will assess whether fine-tuning is the right approach for your use case.