LLM-Powered Document Comparison

Intelligent Document Analysis Using Multi-Agent Systems

The Document Comparison project represents a breakthrough in automated document analysis, utilizing a multi-agent LLM system to intelligently compare documents of any length while maintaining accuracy and preventing hallucinations.

Stage 1: Document Upload Interface

Document Upload Interface

The user interface is designed for simplicity and efficiency, allowing users to: • Upload multiple documents in various formats • Preview document contents before processing • Set comparison parameters and preferences • Track upload and processing status in real-time

Stage 2: Multi-Agent Processing System

Multi-Agent Processing System

The core innovation lies in the multi-agent approach to document comparison: • Documents are strategically segmented for parallel processing • Multiple LLM agents work collaboratively on different sections • Cross-validation between agents ensures accuracy • Dynamic workload distribution based on document complexity • Real-time coordination and consensus building between agents

Stage 3: Structured Comparison Report

Comparison Report Output

The final output provides a comprehensive yet clear comparison: • Hierarchical organization of similarities and differences • Direct text references with context • Confidence scores for each comparison point • Interactive navigation through comparison results • Exportable reports in multiple formats

Technical Breakthroughs

The project overcame several key challenges in document comparison:

Length-Independent Processing

Traditional LLM-based comparison tools struggle with longer documents due to context window limitations. My solution implements a sophisticated segmentation and reconstruction approach that maintains coherence while handling documents of any length.

Hallucination Prevention

The multi-agent system includes built-in verification mechanisms where agents cross-check each other's findings. This dramatically reduces the risk of hallucinated content or incorrect references in the comparison output.

Scalable Architecture

The system architecture is designed to scale horizontally, allowing for: • Parallel processing of multiple document pairs • Dynamic allocation of computational resources • Efficient handling of large document volumes • Real-time processing status updates

This project demonstrates the potential of collaborative AI systems in handling complex document analysis tasks, providing a robust solution for detailed document comparison regardless of length or complexity.