Fine-Tuning & Custom Models

Why it matters

Off-the-shelf LLMs are strong but generic. Fine-tuning (or adapter-based training) teaches a model your vocabulary, style, and constraints so it answers in your tone and makes fewer mistakes on your domain. It’s the next step when prompt engineering and RAG aren’t enough.

What we do

• Data prep and formatting – Turn your docs, Q&A pairs, or logs into training data in the right format (e.g. instruction/response pairs) and help you avoid leakage and bias.
• Model and method choice – Pick base model and approach (full fine-tune, LoRA, or similar) to fit your data size, budget, and latency needs.
• Training and evaluation – Run training (or guide you through your platform), then evaluate on held-out data so you see real gains before deployment.
• Deployment – Export or deploy the tuned model to your API, app, or internal tool and wire up the same interfaces you’d use for any LLM.

When it makes sense

Fine-tuning pays off when you have enough quality data (hundreds to thousands of examples) and need consistent style or domain accuracy. We’ll tell you if RAG or better prompts could get you most of the way first.

Next step

Describe your use case, what data you have, and what “better” looks like (accuracy, tone, compliance). Request support and we’ll outline a training and deployment plan.