AI Fundamentals

Fine-Tuning

Fine-tuning is the targeted retraining of a pre-trained AI model on domain-specific data. The model weights are adjusted so it better masters the terminology, style, or decision logic of a specific industry or task — without training the model from scratch.

Why does this matter?

Fine-tuning makes the difference between a generic AI assistant and one that understands your industry. For companies with specialized vocabulary — such as manufacturing, legal, or medical technology — fine-tuning can improve answer quality by 30-50%. However, it is more resource-intensive than RAG and requires high-quality training data.

How IJONIS uses this

We first evaluate whether RAG or fine-tuning is the better approach — often RAG is sufficient. When fine-tuning is needed, we use LoRA and QLoRA for efficient training on open-source models like Llama and Mistral. We curate training data together with your domain experts.

Frequently Asked Questions

When is fine-tuning worth it compared to RAG?

Fine-tuning is worthwhile when the model needs to master a specific style or technical language (e.g., medical report language). RAG is better when current facts need to be retrieved. In practice, we often combine both approaches for optimal results.

How much training data is needed for fine-tuning?

With LoRA, 500-1,000 high-quality examples often suffice for good results. Quality matters more than quantity. We recommend starting with a small, cleanly curated dataset and improving iteratively.

← Back to Glossary

Want to learn more?

Find out how we apply this technology for your business.

Start AI Readiness Check