What is LLM fine-tuning?

Definition: LLM fine-tuning is the process of further training a pretrained large language model on a curated set of examples so it performs a specific task, format or style more reliably.

How fine-tuning works

Engineers collect and clean example inputs and ideal outputs, then train the model on them. Parameter-efficient methods such as LoRA train only a limited set of added or selected weights, which lowers training cost. Approaches include supervised fine-tuning on labeled examples and preference tuning based on ranked outputs. The tuned model is then compared against the base model on a held-out evaluation set.

Fine-tuning vs RAG

Fine-tuning changes how a model behaves: tone, structure, classification labels or domain-specific reasoning. Retrieval-augmented generation changes what a model knows at answer time by supplying current documents. Many teams start with prompting and RAG, and fine-tune when they need consistent output, lower latency or a lighter, cheaper model.

When a company needs it

Fine-tuning makes sense when a team has enough high-quality examples and a clear, measurable target.

How Ryz helps

Ryz fine-tuning engineers prepare data, train and evaluate models in your cloud, working on your team.

Related

• Hire fine-tuning engineers
• LLM fine-tuning services
• RAG vs fine-tuning
• Retrieval-augmented generation (RAG)