RAG (Retrieval Augmented Generation) System
Advanced RAG implementation for accurate, context-aware AI responses using enterprise knowledge bases.
Project Overview
A sophisticated RAG system that combines retrieval of relevant information from knowledge bases with generative AI to provide accurate, context-aware responses. The system is designed for enterprise use cases where accuracy and source citation are critical.
Key Features
- ✓Vector database integration
- ✓Semantic search capabilities
- ✓Source citation and references
- ✓Multi-document knowledge base
- ✓Real-time knowledge updates
- ✓Custom embedding models
- ✓Query optimization
- ✓Analytics and usage tracking
Technologies Used
Challenges & Solutions
Balancing retrieval accuracy with response quality while maintaining fast response times required optimization of vector search algorithms and careful prompt engineering. We implemented hybrid search combining semantic and keyword matching.
Results
Achieved 95% accuracy in information retrieval, reduced hallucination by 90%, and improved response relevance by 85%. The system now powers multiple enterprise AI applications.