RAG System Optimization - 3x Faster Retrieval

Published

We’re excited to announce a major optimization to our RAG (Retrieval-Augmented Generation) systems—delivering lightning-fast semantic search while reducing infrastructure costs. This update represents a significant leap forward in making enterprise AI knowledge bases more efficient and scalable.

Key Highlights:

  • 3x Faster Retrieval: Optimized vector similarity search now returns relevant documents in under 200ms (down from 600ms)
  • Hybrid Search Algorithm: Combines dense vector search with keyword filtering for 25% improvement in retrieval accuracy
  • Intelligent Caching: New semantic caching layer reduces redundant LLM calls by 50%, cutting API costs significantly
  • Multi-Modal Support: Now supports text, PDF, and web scraping from heterogeneous data sources
  • Automatic Chunking: Smart document chunking adapts to content type, improving context preservation

Performance Metrics:

  • p95 Latency: 180ms (down from 580ms)
  • Cost Per Query: $0.008 (down from $0.015)
  • Retrieval Accuracy: 89% (up from 71%)
  • Supported Document Types: PDF, DOCX, TXT, Markdown, HTML

This update is now available for all enterprise customers and will be automatically deployed to existing RAG systems. Contact our team to learn more about migrating your knowledge base to the optimized architecture.