LLM-Powered Content Classification System

Impact Statement

Building an intelligent content classification system with GPT-4 that achieved 94% accuracy while reducing manual categorization by 85%.

Overview

Chrome extension and backend system for automated content categorization using LLM-powered classification with human review workflows. Developed intelligent system that learns from editorial feedback to continuously improve accuracy.

Key Outcomes

• 94% classification accuracy in production
• 85% reduction in manual categorization time
• Consistent taxonomy application across content
• Scalable system handling 1000+ items daily

Technical Stack

• Extension: Chrome Extension with Manifest V3
• AI Pipeline: LangChain for workflow orchestration
• LLM: OpenAI GPT-4 for content analysis
• Frontend: React for review interface
• Backend: FastAPI for API and workflow management

Challenge Context

Editorial team manually categorizing hundreds of content pieces daily, leading to inconsistent taxonomy application and significant time investment. Need for scalable solution that maintains editorial oversight while dramatically reducing manual work.

Systematic Approach

1. Workflow Analysis

• Editorial team workflow observation and bottleneck identification
• Taxonomy consistency analysis across existing content
• Accuracy requirements and error cost assessment
• Integration requirements with existing tools

2. AI System Design

• LLM prompt engineering with few-shot learning examples
• Confidence scoring system for automated vs. manual review routing
• Chrome extension development for seamless workflow integration
• Feedback loop design to improve model performance over time

3. Development & Testing

• Iterative prompt optimization with editorial team feedback
• Chrome extension testing across different content types
• Accuracy validation with historical data sets
• Cost optimization through intelligent API usage patterns

4. Production Deployment

• Phased rollout with increasing automation thresholds
• Real-time monitoring and adjustment capabilities
• Team training on review workflows and correction processes
• Performance tracking and continuous improvement

Business Impact

Successfully automated 85% of categorization work while maintaining high accuracy standards. Editorial team now focuses on strategic content decisions rather than routine classification tasks, enabling higher content quality and strategic thinking.

Lessons Learned

• Human-in-the-loop design crucial for maintaining quality standards
• Confidence scoring helps optimize AI vs. human resource allocation
• Chrome extensions provide seamless integration into existing workflows
• Continuous learning systems improve over time with proper feedback loops