fine-tuning GPT models in Azure OpenAI?

Question

I'm exploring different ways to fine-tune an AI model for a specific domain using Azure OpenAI. I want to improve response accuracy for industry-specific queries, but I’m unsure whether fine-tuning or embedding-based retrieval would be more effective.

Has anyone experimented with fine-tuning GPT models in Azure OpenAI? How does it compare to using vector search with Azure AI Search for domain-specific responses? Any insights or best practices would be really helpful!

Answer 1

Hi there

Fine-tuning helps if you need the model to learn new patterns or generate industry-specific responses, but it requires significant data and cost. Embedding-based retrieval with Azure AI Search is often more efficient for improving response accuracy, as it allows the model to fetch relevant context dynamically without retraining. If your use case involves frequent updates or large datasets, vector search is the better option.

Accept if this helps thnks much