Model Fine-tuning

Out-of-the-box language models know a lot about the world in general, but little about your specific domain. They may use incorrect terminology, miss industry context, or give answers that are technically correct but wrong for your situation.

Fine-tuning addresses this gap. By training models on your data, we teach them:

Industry vocabulary and concepts. The specialised language and context your work depends on.

Your organisation’s phrasing. The way your teams, products, and policies are described in reality.

Domain knowledge gaps. Where general models repeatedly make wrong assumptions.

Preferred response style and structure. Formats that fit your workflows and reduce post-editing.

The improvement can be substantial. Tasks that generic models handle adequately, fine-tuned models handle well. Tasks that generic models struggle with often become feasible.

Fine-tuning delivers value when:

Domain terminology matters. If your field uses specialised language that general models misunderstand, fine-tuning teaches correct usage.

Accuracy requirements are high. When close enough is not good enough, fine-tuning improves precision in your specific context.

Response style needs consistency. If AI should communicate in a particular way, fine-tuning establishes and maintains that style.

General models underperform. When you have tried prompt engineering and it is not enough, fine-tuning is the next step.

Fine-tuning requires training data that represents your domain. This typically means:

Example interactions: Past conversations, queries and responses, questions and answers. The more examples of good performance, the better the fine-tuning.

Domain documents: Product information, policy documents, technical specifications, knowledge base content. Material that represents accurate domain knowledge.

Quality indicators: Labels identifying good versus poor responses, correct versus incorrect answers. This helps us measure improvement.

We can work with imperfect data. Some cleaning and preparation is usually needed, and we handle that.

Our approach follows a structured methodology.

Data preparation collects, cleans, and formats training material. We verify data quality, remove sensitive information, and structure content appropriately for training.

Baseline evaluation measures how well the unmodified model performs on your tasks. This establishes what we are improving from.

Training adapts the model using your data. We use appropriate techniques depending on the model architecture and your requirements.

Evaluation measures improvement against baseline. We test on held-out examples to verify that fine-tuning generalises rather than just memorising training data.

Iteration refines results if needed. Sometimes multiple rounds of fine-tuning with adjusted parameters produce better outcomes.

Deployment makes the fine-tuned model available for use. We handle integration with your existing systems.

Fine-tuning works with most current large language models, though approaches vary. Some models allow full fine-tuning; others use parameter-efficient techniques. We advise on what is possible and appropriate for your situation.

Fine-tuning creates new model weights that belong to you. These can be deployed on your infrastructure or hosted cloud environments depending on requirements.

Ongoing costs for fine-tuned models are typically similar to base models. The investment is primarily in the fine-tuning process itself.

Fine-tuning is powerful but not always necessary. Simpler approaches sometimes achieve similar results:

Prompt engineering crafts instructions that guide model behaviour without changing the model itself.

Retrieval-augmented generation provides relevant information alongside queries so models can draw on your knowledge base.

Few-shot learning includes examples in prompts to demonstrate desired behaviour.

We help you choose the right approach. Sometimes fine-tuning is overkill; sometimes it is essential.

Use these prompts to decide whether fine-tuning is warranted and what data you need to do it well.

“Where does the base model fail most often in our domain: terminology, formatting, or decision policy?”

“What training examples do we have, and are they high quality enough to teach the model reliably?”

“Should we fine-tune, use retrieval, or combine both to meet the quality bar?”