AI Fine-Tuning & Custom Model development

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.

Regression tests on general tasks, mixing in general data, conservative learning rates and parameter-efficient methods such as LoRA that leave the base model largely intact. Every run is compared against the baseline before it ships. If a task truly needs a very different behaviour, we serve a separate adapter for it rather than pushing one model to do everything.

A focused LoRA fine-tune with existing data can be evaluated within two to four weeks. Projects that require building datasets from scratch or preference tuning typically take six to ten weeks. The schedule is driven mostly by data preparation and evaluation design; the training runs themselves are usually measured in hours.