Best Practices for Training Custom Models in Copilot Studio
Introduction
Training custom AI models in Copilot Studio (formerly Power Virtual Agents) allows businesses to create highly efficient chatbots with customized NLP (Natural Language Processing) capabilities. While Copilot Studio comes with pre-built AI models, fine-tuning and training custom models enhance its accuracy and relevance to specific business needs.
This guide provides a detailed step-by-step approach to training custom models, covering:
✅ Data preparation (gathering and preprocessing training data).
✅ Model training (teaching the chatbot to recognize custom intents).
✅ Testing and validation (ensuring high accuracy and response quality).
✅ Performance optimization (fine-tuning based on real interactions).
Step 1: Understanding Custom Model Training in Copilot Studio
1.1 What is a Custom Model in Copilot Studio?
A custom model in Copilot Studio is an AI system trained to:
- Understand specific business terms.
- Recognize custom intents and entities.
- Improve accuracy and relevance of chatbot responses.
Example Use Cases:
📌 E-commerce Bot: Understands product-related queries (e.g., “Show me summer jackets”).
📌 Healthcare Bot: Recognizes medical symptoms and appointment scheduling.
📌 Financial Bot: Understands loan inquiries and transaction details.
Step 2: Preparing Training Data
Before training the AI model, gather and prepare training data.
2.1 Collect User Queries
- Gather real customer conversations from:
- Chat logs from existing bots.
- Customer service transcripts.
- Website search queries.
- Organize the data into categories:
- Product Inquiries
- Billing Questions
- Technical Support
2.2 Label Training Data (Intent Recognition)
To improve chatbot accuracy, label each query with an intent.
Example:
| User Query | Intent Label |
|---|---|
| “Where is my order?” | Track_Order |
| “I need help with a refund” | Refund_Request |
| “What are the latest discounts?” | Promotions |
2.3 Define Custom Entities
Entities help extract important information from user queries.
Example:
| Entity Type | Example Values |
|---|---|
| Product Name | iPhone 14, Samsung Galaxy |
| Order Number | #12345678, #98765432 |
| Date | March 10, 2024 |
Step 3: Training a Custom Model in Copilot Studio
3.1 Access the Model Training Section
- Log in to Copilot Studio.
- Select your bot and go to Topics → Click Custom AI Models.
- Click Create New Model.
3.2 Train Custom Intents
- Click + New Intent → Give it a name (e.g., Track_Order).
- Add Training Phrases (variations of how users might ask the same question).
Example Training Phrases for “Track_Order” Intent:
✔ “Where is my order?”
✔ “Track my delivery.”
✔ “Has my package been shipped?”
- Click Save & Train.
✅ Now the chatbot can recognize the “Track_Order” intent!
3.3 Add AI Entity Recognition
- Go to Entities → Click Create New Entity.
- Define the entity name (e.g., Order_Number).
- Set the type:
- Prebuilt (Dates, Numbers, Emails).
- Custom (Product Names, Order IDs).
- Click Save & Train.
✅ Now the chatbot can extract order numbers from user queries!
3.4 Train the Model with Variations
To improve performance, add variations to each intent.
- Click Edit Intent → Add more phrases.
- Train the model with:
- Synonyms (e.g., “Refund” = “Money Back”)
- Regional Language Variations (e.g., “Elevator” vs. “Lift”)
- Formal & Informal Phrasing (“Can I get a refund?” vs. “I need my money back!”)
✅ The chatbot is now trained for diverse user inputs!
Step 4: Testing and Evaluating the AI Model
4.1 Use the Built-in Chatbot Simulator
- Open Test Bot on the right panel.
- Enter sample queries to check:
- Correct intent recognition.
- Proper entity extraction.
- Accurate responses.
4.2 Debugging Incorrect Responses
If the chatbot fails, debug the issue:
🔴 Incorrect Intent Recognition?
✔ Add more training phrases.
🔴 Missing Entity Extraction?
✔ Adjust entity rules to recognize new values.
🔴 Confusing Responses?
✔ Use context variables to refine chatbot logic.
Step 5: Deploying and Monitoring the Trained Model
5.1 Deploy the AI Model
- Click Publish in Copilot Studio.
- Select Deployment Channels (Website, Teams, WhatsApp, etc.).
- Click Deploy → The chatbot is now live!
5.2 Monitor AI Performance
- Go to Analytics → Review chatbot interactions.
- Track:
- Intent Recognition Accuracy
- Response Time
- User Satisfaction Ratings
- Click Download Logs → Analyze errors and make improvements.
5.3 Optimize the AI Model Over Time
🚀 Every 30 days, improve the chatbot by:
✅ Adding new training data from real user interactions.
✅ Fixing misclassified intents using analytics.
✅ Updating responses based on user feedback.
Final Thoughts
🚀 Key Takeaways:
✔ Step 1: Gather real user queries and categorize them.
✔ Step 2: Train the chatbot using intents and entities.
✔ Step 3: Use variations, synonyms, and context to improve accuracy.
✔ Step 4: Test, debug, and refine the chatbot before deployment.
✔ Step 5: Monitor performance and continuously improve the AI model.
Would you like a walkthrough on how to connect Copilot Studio with external APIs for real-time responses?