How to Fine-Tune Azure OpenAI Models for Domain-Specific Use Cases?

Question

I'm working on a chatbot using Azure OpenAI and need to fine-tune it for a specific industry (legal). I have a dataset of past conversations and documents. What's the best approach to fine-tune the model while keeping costs manageable? Should I use embeddings with Azure Cognitive Search instead, or is fine-tuning the better option? Any guidance on best practices would be appreciated!

Answer 1

Hi there sam

Thanks for using QandA platform

start by evaluating your dataset of past conversations and documents. Fine-tuning can help the model understand industry-specific language, but it can also be costlier than using embeddings with Azure Cognitive Search. If your use case involves retrieving specific documents or answering domain-related queries, embeddings with Cognitive Search may be more cost-effective and scalable, as they allow the model to leverage pre-built knowledge without needing retraining. However, fine-tuning is better if you need a more customized, conversational response. To keep costs manageable, start by experimenting with embeddings and only fine-tune if you need further refinement in model responses.

Kindly accept if this helps