Fine-tuning AI models in Copilot Studio involves optimizing the chatbot’s responses by customizing the Azure OpenAI model or improving the bot’s behavior within Copilot Studio. While Copilot Studio does not directly allow fine-tuning of OpenAI models like in Azure OpenAI, you can improve performance through data training, API adjustments, and business logic enhancements.

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Approaches to Fine-Tune AI in Copilot Studio

There are two primary ways to fine-tune AI in Copilot Studio:

1. Optimizing the OpenAI Model (via Azure OpenAI)
2. Enhancing Bot Responses in Copilot Studio

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Method 1: Fine-Tuning GPT Models in Azure OpenAI

If you are using GPT-3.5 or GPT-4 via Azure OpenAI API, fine-tuning allows the model to generate more business-specific responses.

1.1 Prepare Training Data

Fine-tuning requires a dataset formatted in JSONL (.jsonl), with examples of user prompts and ideal responses.

Example of JSONL Format:

{"messages": [{"role": "system", "content": "You are a customer support bot."}, {"role": "user", "content": "How do I reset my password?"}, {"role": "assistant", "content": "You can reset your password by clicking 'Forgot Password' on the login page."}]}
{"messages": [{"role": "system", "content": "You are a banking support assistant."}, {"role": "user", "content": "What is my account balance?"}, {"role": "assistant", "content": "I’m sorry, but I cannot access account details. Please log in to your banking portal."}]}

💡 Tip: Include at least 100-500 examples for better accuracy.

1.2 Upload Training Data to Azure OpenAI

1. Open Azure Portal → Navigate to Azure OpenAI Service.
2. Select Fine-tuning from the left menu.
3. Click Upload File and select your .jsonl file.
4. Start the fine-tuning job and wait for completion.

1.3 Deploy Fine-Tuned Model

1. Once training is complete, deploy the fine-tuned model.
2. Copy the deployment name (you’ll need this for API calls).

1.4 Update API Calls in Copilot Studio

1. Open Copilot Studio.
2. Navigate to Topics → Add API Call.
3. Update the request to use the fine-tuned model: { "model": "ft-your-model-name", "messages": [{"role": "user", "content": "{User Input}"}], "max_tokens": 150 }
4. Save and test.

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Method 2: Enhancing Bot Responses in Copilot Studio

If you are not fine-tuning via Azure OpenAI, you can optimize the chatbot’s behavior directly within Copilot Studio.

2.1 Train the AI with Improved Topics

1. Open Copilot Studio → Topics.
2. Click New Topic → Add Trigger Phrases.
3. Provide variations of how users might ask the same question. Example for Order Tracking:
   • “Where is my order?”
   • “Can you track my package?”
   • “Check my delivery status.”
4. Provide a clear and structured bot response.
5. Save and publish changes.

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2.2 Adjust Bot’s AI Understanding (Custom GPT Prompts)

1. Open a Topic in Copilot Studio.
2. Use the Generative AI response feature.
3. Improve responses by using structured prompt templates: Example for FAQ: Act as a professional customer support agent. Answer in a friendly and concise manner.
4. Test responses and refine as needed.

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2.3 Use Adaptive Cards for Better User Engagement

Instead of just plain text responses, improve interaction by using Adaptive Cards:

1. Go to Bot Responses.
2. Click Add Card → Choose Adaptive Card.
3. Format responses with buttons, images, or quick actions.

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2.4 Use Power Automate for Complex Workflows

To automate external requests, use Power Automate:

1. Open Power Automate → Create Flow.
2. Select Trigger from Copilot Studio.
3. Add a step for calling APIs, retrieving databases, or integrating with CRM.
4. Return data dynamically to Copilot Studio.

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Final Thoughts

Fine-tuning AI in Copilot Studio can be achieved by:

• Using a fine-tuned GPT model via Azure OpenAI
• Enhancing chatbot training with improved topics & responses
• Leveraging Power Automate for advanced actions

Would you like help with specific use cases or API integrations?