Comprehensive security and quality evaluation capabilities for LLM applications with input/output scanning and advanced evaluation metrics.

LLM Judge Block

The LLM Judge block provides comprehensive security and quality evaluation capabilities for LLM applications. It offers input/output scanning for safety and policy enforcement, plus advanced evaluation metrics to assess response quality. Select an operation from the Choose Operation dropdown to begin.

Overview

The LLM Judge block is a comprehensive evaluation system designed to ensure the safety, quality, and compliance of Large Language Model (LLM) applications. It provides multiple evaluation capabilities including content safety scanning, policy enforcement, and quality assessment metrics.

Configuration Options

Operation Types

Choose the type of evaluation operation:

Input Safety Scan: Scan input prompts for safety violations and policy breaches
Output Quality Assessment: Evaluate LLM response quality and accuracy
Content Moderation: Moderate content for inappropriate or harmful material
Policy Compliance: Check compliance with organizational policies
Bias Detection: Detect potential bias in LLM responses
Hallucination Detection: Identify potential hallucinations or false information

Safety Configuration

Safety Categories: Configure safety categories to monitor (violence, hate speech, etc.)
Severity Levels: Set severity thresholds for different types of violations
Policy Rules: Define custom policy rules and compliance requirements
Action Triggers: Configure actions to take when violations are detected

Quality Metrics

Accuracy Assessment: Evaluate response accuracy and factual correctness
Relevance Scoring: Measure how relevant responses are to input prompts
Coherence Analysis: Assess response coherence and logical flow
Completeness Check: Verify response completeness and thoroughness

How It Works

The LLM Judge block:

Receives Content: Gets input prompts or LLM responses to evaluate
Applies Evaluation: Uses selected operation to analyze the content
Generates Scores: Produces safety and quality scores
Returns Results: Sends evaluation results with recommendations

Evaluation Flow

Content Input → Operation Selection → Evaluation → Scoring → Results & Recommendations

Use Cases

Content Safety

Ensure safe content generation:

LLM input → LLM Judge (safety scan) → safe input → LLM processing

Quality Assurance

Assess LLM response quality:

LLM output → LLM Judge (quality assessment) → quality score → decision

Policy Enforcement

Enforce organizational policies:

content → LLM Judge (policy compliance) → compliance status → action

Bias Monitoring

Monitor for bias in LLM responses:

LLM output → LLM Judge (bias detection) → bias report → mitigation

Common Patterns

Input Safety Scanning

// Configuration
Operation: Input Safety Scan
Safety Categories: ["violence", "hate_speech", "harassment"]
Severity Threshold: "medium"
Action: "block"

// Input: "How to make a bomb"
// Output: {
//   safe: false,
//   violations: ["violence"],
//   severity: "high",
//   action: "block",
//   reason: "Contains violent content"
// }

Output Quality Assessment

// Configuration
Operation: Output Quality Assessment
Metrics: ["accuracy", "relevance", "coherence"]
Quality Threshold: 0.8
Action: "flag_low_quality"

// Input: LLM response
// Output: {
//   quality_score: 0.85,
//   metrics: {
//     accuracy: 0.9,
//     relevance: 0.8,
//     coherence: 0.85
//   },
//   recommendation: "accept"
// }

Policy Compliance Check

// Configuration
Operation: Policy Compliance
Policies: ["data_privacy", "confidentiality", "professional_conduct"]
Compliance Threshold: 0.9
Action: "review_required"

// Input: Content to check
// Output: {
//   compliant: true,
//   compliance_score: 0.95,
//   policy_violations: [],
//   recommendation: "approve"
// }

Advanced Features

Custom Policy Definition

Define custom policies and rules:

Rule Creation: Create custom evaluation rules and policies
Policy Templates: Use pre-built policy templates for common use cases
Rule Validation: Validate policy rules for correctness and completeness
Policy Versioning: Track and manage policy versions and changes

Real-time Monitoring

Monitor LLM applications in real-time:

Streaming Analysis: Analyze content streams in real-time
Alert System: Set up alerts for policy violations or quality issues
Dashboard Integration: Integrate with monitoring dashboards
Historical Analysis: Track trends and patterns over time

Advanced Analytics

Comprehensive evaluation analytics:

Performance Metrics: Track evaluation performance and accuracy
Trend Analysis: Analyze trends in safety and quality over time
Comparative Analysis: Compare different LLM models or configurations
Reporting: Generate detailed evaluation reports

Configuration Examples

Content Moderation System

// Configuration
Operation: Content Moderation
Categories: ["inappropriate", "spam", "misinformation"]
Severity Levels: ["low", "medium", "high", "critical"]
Actions: {
  low: "flag",
  medium: "review",
  high: "block",
  critical: "block_and_alert"
}

// Use case: Moderate user-generated content

Quality Assurance Pipeline

// Configuration
Operation: Output Quality Assessment
Metrics: ["accuracy", "relevance", "coherence", "completeness"]
Quality Threshold: 0.8
Actions: {
  high_quality: "approve",
  medium_quality: "review",
  low_quality: "reject"
}

// Use case: Ensure high-quality LLM responses

Compliance Monitoring

// Configuration
Operation: Policy Compliance
Policies: ["GDPR", "HIPAA", "SOX", "internal_policies"]
Compliance Threshold: 0.95
Action: "audit_trail"

// Use case: Ensure regulatory compliance

Tips

Define Clear Policies: Establish clear and specific policies for evaluation
Set Appropriate Thresholds: Configure thresholds based on your risk tolerance
Monitor Performance: Regularly monitor evaluation performance and accuracy
Update Policies: Keep policies updated with changing requirements
Handle Edge Cases: Consider how to handle unusual or ambiguous content
Document Decisions: Maintain audit trails of evaluation decisions

Common Issues

False Positives

Issue: Safe content being flagged as unsafe
Solution: Adjust safety thresholds and review policy definitions

False Negatives

Issue: Unsafe content not being detected
Solution: Strengthen safety rules and lower detection thresholds

Performance Impact

Issue: Evaluation causing delays in LLM processing
Solution: Optimize evaluation algorithms and use caching

Policy Conflicts

Issue: Conflicting policies causing evaluation issues
Solution: Review and resolve policy conflicts

Performance Considerations

Evaluation Speed

Algorithm Selection: Choose efficient evaluation algorithms
Caching: Cache evaluation results for repeated content
Parallel Processing: Use parallel processing for batch evaluations
Resource Optimization: Optimize resource usage for better performance

Accuracy vs Speed

Threshold Tuning: Balance between evaluation accuracy and speed
Model Selection: Choose appropriate models for your use case
Batch Processing: Process multiple items together for efficiency
Quality Monitoring: Monitor evaluation quality and adjust as needed

LLM Query - For LLM interactions that need evaluation
Hallucination Detector - For detecting hallucinations
function - For custom evaluation logic
debug - For monitoring evaluation results

LLM Judge

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