Fine-tuning trains a pre-existing AI model on your specific data so it performs better on your use case than a general-purpose model. It makes sense when you need highly consistent tone, domain-specific accuracy, or behavior that prompt engineering alone cannot achieve.

You will typically need a dataset of input-output pairs relevant to your use case, usually in JSONL format. The quality and relevance of your training data has the largest impact on fine-tuning results. Freelancers can advise on minimum dataset size and structure.

Freelancers can fine-tune open-source models like Llama, Mistral, and Falcon, as well as proprietary models that offer fine-tuning APIs such as OpenAI's GPT models. The best choice depends on your performance requirements, budget, and deployment environment.