AI Agent Knowledge Base Evaluation System
An advanced AI agent evaluation system developed for PineAI, featuring multi-dimensional assessment capabilities, data anonymization, and automated knowledge extraction.
Project Overview
This project involved building a comprehensive evaluation system from scratch for assessing AI Agent knowledge bases. The system incorporates advanced data processing, automated evaluation, and concurrent processing capabilities to enhance AI agent performance assessment.
Technical Stack
- Core Technologies: Python, Shell Scripting
- AI/ML: LLM APIs, Prompt Engineering, LLM-as-a-judge architecture
- Data Processing: Automated data anonymization algorithms
- Evaluation: Multi-dimensional assessment framework
- Architecture: Concurrent processing for scalability
Key Components
Data Anonymization Module
- Processed 1,000+ conversation texts
- Achieved 70%+ accuracy in sensitive information identification
- Automated pipeline for data sanitization and privacy protection
Q&A Extraction System
- Built standardized prompt engineering workflows
- Implemented LLM-powered automatic Q&A extraction
- Achieved 80%+ knowledge extraction accuracy
- Scalable processing for large conversation datasets
Multi-Dimensional Evaluation Engine
- LLM-as-a-judge model implementation
- Concurrent processing architecture for efficiency
- Comprehensive evaluation metrics and scoring
- Real-time assessment capabilities
Technical Achievements
System Architecture
- Modular Design: Separable components for flexibility
- Concurrent Processing: Optimized for handling multiple evaluation tasks simultaneously
- Scalable Infrastructure: Built to handle growing evaluation workloads
- Automated Workflows: Reduced manual intervention through intelligent automation
Performance Metrics
- Data Processing: Successfully anonymized 1,000+ conversation sessions
- Extraction Accuracy: 80%+ knowledge extraction rate
- Identification Accuracy: 70%+ sensitive information detection
- System Impact: Achieved 0-1 improvement in automated evaluation systems
Innovation Highlights
- Novel Evaluation Framework: Multi-dimensional assessment approach for AI agents
- Privacy-Preserving Processing: Advanced anonymization while maintaining data utility
- Automated Knowledge Extraction: Intelligent parsing of conversational data
- Concurrent Evaluation: Parallel processing for enhanced performance
Impact and Applications
This system significantly improved PineAI’s ability to assess and enhance their AI agent capabilities. The automated evaluation framework provides valuable insights into agent performance while maintaining data privacy standards.
Skills Demonstrated
- AI Agent Evaluation: Comprehensive understanding of AI system assessment
- Prompt Engineering: Advanced prompt design for optimal LLM performance
- Data Privacy: Implementation of robust anonymization techniques
- System Architecture: Design of scalable, concurrent processing systems
- LLM Integration: Effective use of large language models for automated evaluation