Quality Training Data: Train the model on high-quality, up-to-date company documents, ensuring it reflects accurate information.
Fine-tuning: Regularly fine-tune the model on specific support use cases and real customer interactions.
Feedback Loops: Implement a system for human oversight where support agents can review and correct the AI's responses.
Context Awareness: Design the system to ask clarifying questions if uncertain, avoiding direct false information.
Monitoring: Continuously monitor and evaluate the AI’s performance to catch and address any issues promptly.
Quality Training Data: Train the model on high-quality, up-to-date company documents, ensuring it reflects accurate information.
Fine-tuning: Regularly fine-tune the model on specific support use cases and real customer interactions.
Feedback Loops: Implement a system for human oversight where support agents can review and correct the AI's responses.
Context Awareness: Design the system to ask clarifying questions if uncertain, avoiding direct false information.
Monitoring: Continuously monitor and evaluate the AI’s performance to catch and address any issues promptly.