LLM RAG accuracy evaluation
Evaluates retrieval accuracy and answer relevance, detects hallucination risks, and provides tuning guidance for continuous improvement.
LLM RAG Evaluation and Performance Monitoring
EZIS for LLM RAG combines accuracy evaluation with operational monitoring to improve the reliability and performance of LLM RAG services.
LLM RAG Vector DB monitoring
Monitors Vector DB metrics and document retrieval in real time and helps optimize hyperparameters for better matching and answer accuracy.
LLM RAG operations management
Tracks LangChain APIs, server CPU and memory, active users, request volume, average response time, and error rates in one view.
Product
Automated LLM RAG evaluation
Evaluates answer relevance, semantic agreement with ground truth, answer similarity, retrieved-context relevance and coverage, context utilization, and groundedness.
Product
LLM RAG operations management
Automatically generates test questions to validate retrieval performance and compares vector similarity and distance across preprocessing, embedding, and indexing methods. The selected metrics become baselines for production analysis.
Data sources
1Create questions
2Create user answers
3RAG stack
4Compare with ground truth
- 01Generate test questions automatically
- 02Select source documents
- 03Validate retrieval with generated questions
Product
Vector DB monitoring
Visualizes average vector similarity and key server metrics, then uses alerts to identify answer-quality and retrieval-error risks. Query and retrieved-document IDs are logged for continuous quality management.
- Token usage
- Vector similarity and distance
- LLM answer generation time
- Vector DB query time
- Server metrics including CPU, memory, and GPU