When to Use a Custom Model in Azure OpenAI?
While Azure OpenAI provides powerful pre-trained models like GPT-4, GPT-3.5, Codex, and DALL·E, there are cases where fine-tuning a custom model is beneficial.
- When to Use a Custom Model
✅ Domain-Specific Language & Terminology
• If your industry uses specialized jargon (e.g., medical, legal, finance), a fine-tuned model improves accuracy.
✅ Consistent Brand Voice & Style
• If you need AI to follow specific writing guidelines, tone, or brand voice, training on your dataset ensures consistency.
✅ Handling Proprietary Data & Context
• If your application requires AI to understand and generate responses based on company-specific knowledge (e.g., internal documents, policies, FAQs).
✅ Reducing Hallucinations & Improving Accuracy
• Pre-trained models sometimes generate inaccurate responses. Fine-tuning helps focus responses on factual and relevant data.
✅ Optimizing for Specific Tasks
• If your use case requires structured outputs like financial reports, legal contracts, or chatbot responses, fine-tuning improves efficiency.
✅ Avoiding Prompt Engineering Complexity
• Instead of using long and complex prompts, a fine-tuned model understands patterns in your data directly, making API calls more efficient.
How to Train a Custom Model in Azure OpenAI?
Step 1: Prepare Your Dataset
• Format: JSONL (JSON Lines)
• Structure: Each line in the file should be a training example in this format:
{“messages”: [{“role”: “system”, “content”: “You are a legal document assistant.”}, {“role”: “user”, “content”: “Summarize this contract.”}, {“role”: “assistant”, “content”: “This contract outlines the obligations of both parties…”}]}
• Include high-quality examples relevant to your task.
• The dataset should be diverse and well-labeled to avoid biases.
Step 2: Upload Data to Azure
1. Go to Azure OpenAI Studio.
2. Navigate to Fine-tuning → Upload Dataset.
3. Select your JSONL file and upload it.
Step 3: Train the Model
1. Choose the base model (e.g., GPT-3.5 or GPT-4).
2. Configure training settings:
• Epochs: Number of training passes over the dataset.
• Batch Size: Number of examples processed together.
• Learning Rate: Controls how much the model updates per training step.
3. Start the training process (this may take several hours).
Step 4: Evaluate & Test the Model
• Use test prompts to see how well the fine-tuned model performs.
• Compare responses against the base model to check improvements.
• Refine and retrain if necessary.
Step 5: Deploy & Integrate the Model
1. Deploy the fine-tuned model via Azure OpenAI API.
2. Integrate with your application using REST API or SDKs.
3. Monitor model performance and retrain periodically for improvements.
When Not to Train a Custom Model?
❌ If simple prompt engineering can achieve the same results.
❌ If the dataset is too small (fine-tuning requires a substantial dataset).
❌ If the cost of training is too high compared to API-based solutions.
Sample Dataset for Fine-Tuning a Custom Model in Azure OpenAI
Azure OpenAI fine-tuning requires a JSONL (JSON Lines) file, where each line represents a training example with a structured conversation.
Example: JSONL Dataset for a Customer Support Chatbot
Each entry consists of a system instruction, a user query, and an AI response.
{“messages”: [{“role”: “system”, “content”: “You are a customer support assistant for an e-commerce website.”}, {“role”: “user”, “content”: “How can I track my order?”}, {“role”: “assistant”, “content”: “You can track your order by visiting the ‘My Orders’ section and entering your tracking number.”}]}
{“messages”: [{“role”: “system”, “content”: “You are a customer support assistant for an e-commerce website.”}, {“role”: “user”, “content”: “What is your return policy?”}, {“role”: “assistant”, “content”: “Our return policy allows returns within 30 days of delivery. Please ensure the item is in its original condition.”}]}
{“messages”: [{“role”: “system”, “content”: “You are a customer support assistant for an e-commerce website.”}, {“role”: “user”, “content”: “Do you offer international shipping?”}, {“role”: “assistant”, “content”: “Yes, we offer international shipping to selected countries. You can check availability at checkout.”}]}
Steps to Fine-Tune in Azure OpenAI
- Prepare Your Dataset
- • Format your dataset in JSONL format like the example above.
- • Ensure consistency in examples and include diverse scenarios.
- • Save the file as training_data.jsonl.
2. Upload Dataset to Azure
1. Go to Azure OpenAI Studio → Fine-tuning → Upload Dataset.
2. Upload training_data.jsonl.
3. Validate the dataset (Azure will check for formatting errors).
3. Start Fine-Tuning
1. Select a base model (e.g., GPT-3.5-Turbo).
2. Configure:
• Epochs: Start with 3–5 for small datasets.
• Batch Size: Let Azure auto-select or manually choose based on dataset size.
• Learning Rate: Default is recommended unless optimizing for specific needs.
3. Start the fine-tuning process (may take several hours).
4. Test and Evaluate the Model
• Once training is complete, test the model using:
curl -X POST https://YOUR_AZURE_ENDPOINT/openai/deployments/YOUR_CUSTOM_MODEL/completions?api-version=2024-02-01 \
-H “Content-Type: application/json” \
-H “Authorization: Bearer YOUR_API_KEY” \
-d ‘{“messages”: [{“role”: “user”, “content”: “How can I track my order?”}]}’
• Compare responses to ensure improvements.
• Retrain with additional data if needed.
5. Deploy & Use in Applications
• Deploy the fine-tuned model in Azure OpenAI Studio.
• Integrate with REST APIs, chatbots, or web apps.
