llm-evaluation

Implement evaluation strategies for LLM applications using automated metrics, human feedback, and LLM-as-judge approaches.

• Automated metrics (BLEU, ROUGE, BERTScore, accuracy, precision/recall/F1)
• Human evaluation (accuracy, coherence, relevance, safety, helpfulness)
• LLM-as-judge (pointwise, pairwise, reference-based comparisons)
• Benchmarking for performance, regression detection, and quality assurance

• Systematic performance measurement
• Scalable automated scoring with metrics like BLEU and ROUGE
• Human-in-the-loop for quality dimensions
• LLM-based judgment of output quality
• Support for grounding, toxicity, and factuality checks
• Baseline establishment and progress tracking

1. 1.Measure model performance in testing
2. 2.Compare model variants or prompt changes
3. 3.Detect performance regressions
4. 4.Validate improvements from prompt tuning
5. 5.Build confidence in production systems
6. 6.Debug unexpected behaviors
7. 7.Establish evaluation frameworks