High Tech — Automated Retraining of RAG Models

Overview

The purpose of this DAG is to automate the retraining process of Retrieval-Augmented Generation (RAG) models utilized by AI assistants in contact centers. It ingests recent interaction data from various sources, including chat logs and customer feedback, to evaluate the performance of existing models. If the performance metrics fall below predefined thresholds, the DAG triggers the retraining process. This involves processing the new data to refine the models, ensuring they are aligned with the

The purpose of this DAG is to automate the retraining process of Retrieval-Augmented Generation (RAG) models utilized by AI assistants in contact centers. It ingests recent interaction data from various sources, including chat logs and customer feedback, to evaluate the performance of existing models. If the performance metrics fall below predefined thresholds, the DAG triggers the retraining process. This involves processing the new data to refine the models, ensuring they are aligned with the latest user interactions and trends. Quality controls are integral to the workflow, incorporating validation checks on the retrained models to guarantee their effectiveness before deployment. The updated models are then seamlessly integrated into the existing system, providing enhanced responses and improved customer satisfaction. Key performance indicators (KPIs) monitored throughout this process include retraining time, model performance improvement, and user engagement metrics. By automating this workflow, businesses can achieve significant operational efficiency and maintain a competitive edge in the high-tech industry, ensuring their AI assistants remain relevant and effective.

Part of the AI Assistants & Contact Center solution for the High Tech industry.

Use cases

• Enhanced AI assistant performance through continuous learning
• Reduced manual intervention, saving time and resources
• Improved customer satisfaction with accurate responses
• Adaptability to changing user behavior and preferences
• Increased operational efficiency in contact center workflows