In Azure OpenAI Service, fine-tuning refers to the process of uploading your own labeled dataset to customize or adapt a pretrained model (such as GPT-3.5 or Curie) for a specific use case. According to the Microsoft Learn documentation and AI-900 official study guide, fine-tuning allows organizations to improve a model’s performance on domain-specific tasks or to align responses with brand tone and context.
Fine-tuning differs from simple prompting because it requires providing structured training data (usually in JSONL format) that contains pairs of input prompts and ideal completions. The model uses this data to adjust its internal weights, thereby “learning” your organization’s language patterns, terminology, or industry context.
Option review:
A. Completion: Refers to the text generated by a model in response to a prompt. It’s the output, not the customization process.
B. Grounding: Integrates external, up-to-date data sources (like search results or databases) during inference but doesn’t alter the model’s parameters.
C. Fine-tuning: ✅ Correct — this is the process of uploading and training with your own data.
D. Prompt engineering: Involves designing effective prompts but does not change the underlying model.
Thus, fine-tuning is the term used for customizing an Azure OpenAI model using your own uploaded data.
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