RAG System

Problem: Evaluating RAG in Production

Production RAG systems are hard to evaluate. Deploying an optimization looks good in testing but degrades performance on real-world queries. Most teams rely on manual evaluation or naive accuracy metrics that miss latency, token cost, and hallucination risks.

Solution: Evaluation Pipeline

How It Works

• Test 3 retrieval strategies on your RAG system
• Measure accuracy, latency, and cost for each strategy
• Detect regressions before deploying to production
• Get automated alerts on quality drops or cost spikes

Evaluation Metrics

• Automated metrics: relevance, completeness, latency, token cost
• LLM-as-judge: qualitative scoring with Claude
• Regression detection: A/B testing with statistical significance
• Audit trail: Full traceability for compliance

Comparative Dashboard

• React frontend for real-time visualization
• Side-by-side comparison of all strategies
• Deep-dive into latency, tokens, reasoning traces
• Export & reporting for stakeholders

Results

• Automated regression detection before user impact
• Data-driven strategy selection per query class
• Community contribution: helps teams build better RAG systems
• Multi-dimensional evaluation: latency, cost, and quality

Technology Stack