High Tech — Feature Engineering for RAG Model Enhancement
NewThis DAG focuses on feature engineering to enhance RAG models used by AI assistants. It processes historical interaction data to improve model accuracy and efficiency.
Overview
The primary purpose of this DAG is to optimize feature engineering for RAG models utilized in AI assistants and contact centers. By ingesting historical interaction data, the DAG enhances the models' performance through rigorous data transformation and feature selection techniques. The data sources include interaction logs, customer feedback records, and agent performance metrics. The ingestion pipeline systematically collects and preprocesses these data sources, ensuring they are clean and rele
The primary purpose of this DAG is to optimize feature engineering for RAG models utilized in AI assistants and contact centers. By ingesting historical interaction data, the DAG enhances the models' performance through rigorous data transformation and feature selection techniques. The data sources include interaction logs, customer feedback records, and agent performance metrics. The ingestion pipeline systematically collects and preprocesses these data sources, ensuring they are clean and relevant for analysis. Processing steps include data normalization, feature extraction, feature selection, and model validation. Quality control measures are implemented at each stage, including data validation checks and performance assessments of the models to ensure reliability and accuracy. The outputs of this DAG consist of refined feature sets and trained models ready for deployment. Monitoring key performance indicators (KPIs) such as model accuracy improvements and data processing time is crucial to evaluate the effectiveness of the feature engineering process. The business value derived from this DAG includes enhanced model performance, improved customer interaction quality, and increased operational efficiency, ultimately leading to better customer satisfaction and retention.
Part of the AI Assistants & Contact Center solution for the High Tech industry.
Use cases
- Increased accuracy of AI models leading to better customer service
- Faster response times through optimized processing
- Enhanced decision-making capabilities for agents
- Improved customer insights from refined data analytics
- Higher operational efficiency reducing costs and time
Technical Specifications
Inputs
- • Historical interaction logs from customer service
- • Customer feedback records from surveys
- • Agent performance metrics from internal systems
Outputs
- • Refined feature sets for model training
- • Trained RAG models ready for deployment
- • Performance reports on model accuracy
Processing Steps
- 1. Ingest historical interaction data
- 2. Normalize and preprocess data
- 3. Extract relevant features from datasets
- 4. Select optimal features for model training
- 5. Validate data quality and model performance
- 6. Train RAG models with refined features
- 7. Generate performance reports for monitoring
Additional Information
DAG ID
WK-1049
Last Updated
2026-01-21
Downloads
105