📊 Retrieval Quality Analyzer
Stop guessing whether your retrieval is working. Get quantified metrics on every query.
Query-Chunk Alignment Visualization
See exactly how your queries match retrieved chunks:
- Visual connection lines between queries and chunks
- Line thickness represents vector similarity scores
- Color coding shows semantic coverage depth
- Instantly identify misaligned retrievals
Top-K Decay Curve Analysis
Understand how relevance drops across your top-K results:
- Flat curves indicate poor discrimination between relevant and irrelevant chunks
- Steep drops show only the first few results are useful
- Optimize your K parameter based on actual performance data
- Compare different retrieval strategies side-by-side
Precision & Recall Metrics
Track the metrics that matter:
- Precision@K: How many retrieved chunks are actually relevant
- Recall@K: What percentage of relevant information was captured
- F1 Score: Balanced measure of retrieval effectiveness
- Historical trending to track improvements over time
🔬 Context Pollution Tracker
Identify and eliminate noise in your context window before it causes hallucinations.
Pollution Heatmap
Visualize exactly where noise enters your prompts:
- Red highlighting shows irrelevant or contradictory text segments
- Intensity indicates pollution severity
- Click any segment to see why it was flagged
- Export annotated prompts for team review
Signal-to-Noise Ratio Dashboard
Quantify context quality with precision:
- Real-time SNR calculation for every request
- Threshold alerts when noise exceeds acceptable levels
- Breakdown by chunk source and retrieval method
- Correlation analysis with model output quality
Attention Weight Analysis
See what your LLM is actually focusing on:
- Overlay model attention weights on your context
- Identify when models focus on polluted segments
- Detect "distraction patterns" that lead to errors
- Validate that important information receives proper attention
🎯 The "Needle" Finder
Automated stress testing to find your system's breaking points.
Automated Needle-in-Haystack Testing
Systematically test retrieval robustness:
- Insert known facts into documents of varying lengths
- Test if your system can accurately retrieve them
- Identify the exact context length where performance degrades
- Detect "Lost in the Middle" phenomena
Parameter Sensitivity Analysis
Understand how configuration affects performance:
- Test different chunk sizes and overlap settings
- Vary K values and reranking thresholds
- Compare embedding models and distance metrics
- Generate optimization recommendations
Stress Test Reports
Comprehensive analysis of system limits:
- Success rate across different document lengths
- Performance degradation curves
- Failure pattern analysis
- Actionable recommendations for improvement
🔄 Diff Comparison Tool
Compare retrieval strategies pixel-by-pixel to make data-driven decisions.
Strategy Comparison
- Side-by-side comparison of different retrieval methods
- Vector search vs. hybrid search vs. keyword search
- Pollution resistance comparison
- Performance and cost trade-off analysis
A/B Testing Framework
- Run controlled experiments on live traffic
- Statistical significance testing
- Automatic winner detection
- Gradual rollout capabilities
âš¡ Real-Time Monitoring
Stay on top of your RAG system's health 24/7.
Live Metrics Dashboard
- Real-time precision, recall, and pollution metrics
- Request log streaming with anomaly detection
- Automatic alerting for quality degradation
- Custom metric definitions and thresholds
Request Inspector
- Drill down into any individual request
- Full trace from query to response
- Chunk-level analysis and scoring
- Replay and debug problematic requests