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LLM Evaluation

A comprehensive skill for testing, benchmarking, and validating large language model outputs. This skill provides frameworks for measuring LLM performance across quality, safety, and reliability dimensions.

Status: 🔵 Master Plan Available

Key Topics​

  • Evaluation Frameworks

    • Automated metrics (BLEU, ROUGE, perplexity)
    • Model-based evaluation (LLM-as-judge)
    • Human evaluation protocols
    • Benchmark datasets and leaderboards

  • Quality Dimensions

    • Accuracy and factual correctness
    • Relevance and coherence
    • Fluency and readability
    • Instruction following
    • Reasoning capabilities

  • Safety & Alignment

    • Harmful content detection
    • Bias and fairness testing
    • Jailbreak resistance
    • Privacy and PII handling
    • Hallucination detection

  • Production Monitoring

    • Regression testing
    • A/B test design
    • Confidence scoring
    • Error analysis workflows
    • User feedback integration

Primary Tools & Technologies​

  • Evaluation Frameworks: OpenAI Evals, EleutherAI LM Evaluation Harness, HELM
  • LLM-as-Judge: GPT-4, Claude, custom evaluation models
  • Benchmarks: MMLU, HumanEval, TruthfulQA, BBH, GSM8K
  • Testing Tools: PromptFoo, DeepEval, Ragas (RAG evaluation)
  • Observability: LangSmith, Helicone, Arize AI, Traceloop
  • Human Annotation: Label Studio, Prodigy, Scale AI

Integration Points​

  • Prompt Engineering: Iterative refinement based on evaluation
  • MLOps: CI/CD integration, regression testing
  • Monitoring: Production performance tracking
  • Content Moderation: Safety evaluation pipelines
  • User Research: Human feedback collection

Evaluation Methodologies​

Automated Metrics​

Quantitative measurements:
- BLEU, ROUGE: Text similarity
- Perplexity: Model confidence
- BERTScore: Semantic similarity
- Exact Match: Precision tasks
- F1 Score: Information extraction

LLM-as-Judge​

Model-based evaluation:
- Pairwise comparison (Model A vs B)
- Single-answer grading (0-10 scale)
- Rubric-based assessment
- Multi-dimensional scoring
- Chain-of-thought explanations

Human Evaluation​

Expert and crowd-sourced review:
- Side-by-side comparison
- Likert scale ratings
- Task completion success
- Qualitative feedback
- Edge case identification

RAG-Specific Evaluation​

Retrieval-augmented systems:
- Context relevance
- Answer faithfulness
- Groundedness (citation accuracy)
- Context utilization
- Retrieval precision/recall

Evaluation Dimensions​

Quality Metrics​

  • Accuracy: Factual correctness of information
  • Relevance: Alignment with user intent
  • Completeness: Coverage of required information
  • Coherence: Logical flow and consistency
  • Conciseness: Appropriate length and clarity

Safety Metrics​

  • Toxicity: Offensive or harmful content
  • Bias: Unfair treatment of groups
  • Privacy: PII leakage or exposure
  • Jailbreaks: Circumvention attempts
  • Refusals: Appropriate boundary-setting

Performance Metrics​

  • Latency: Time to first token, total generation time
  • Throughput: Requests per second
  • Cost: Tokens consumed per request
  • Reliability: Success rate, error frequency
  • Consistency: Output stability across similar inputs

Testing Workflows​

Pre-Deployment Testing​

1. Unit Tests → Individual prompt variations
2. Integration Tests → End-to-end workflows
3. Benchmark Tests → Standard dataset evaluation
4. Safety Tests → Red teaming, adversarial inputs
5. Human Review → Expert validation
6. A/B Test Design → Production experiment setup

Production Monitoring​

1. Sample Traffic → Representative request subset
2. Automated Scoring → Real-time quality metrics
3. Anomaly Detection → Drift and degradation
4. User Feedback → Thumbs up/down, reports
5. Periodic Audits → Comprehensive reviews
6. Regression Tests → Version comparison

Best Practices​

  • Multiple Evaluation Methods: Combine automated, model-based, and human evaluation
  • Representative Test Sets: Cover diverse use cases and edge cases
  • Calibration: Validate automated metrics against human judgment
  • Continuous Monitoring: Track performance over time
  • Version Control: Maintain evaluation datasets and results
  • Clear Rubrics: Define explicit evaluation criteria
  • Blind Testing: Prevent bias in comparative evaluations
  • Statistical Significance: Use adequate sample sizes

Common Pitfalls​

  • Over-relying on single metrics (e.g., only accuracy)
  • Insufficient test set diversity
  • Evaluation-training data leakage
  • Ignoring latency and cost in evaluation
  • Not testing failure modes and edge cases
  • Lack of baseline comparisons
  • Poor inter-annotator agreement in human eval
  • Evaluating on outdated or biased benchmarks

Success Metrics​

  • Test Coverage: Percentage of use cases with evaluations
  • Evaluation Velocity: Time from development to validated results
  • Quality Score: Aggregate performance across dimensions
  • Pass Rate: Percentage meeting quality thresholds
  • Regression Detection: Catching performance degradation
  • Cost Efficiency: Evaluation cost per model improvement