llm-eval

What problem does it solve?

This Skill addresses the critical need for robust evaluation of Large Language Model (LLM) applications, ensuring quality, performance, and reliability.

Core Features & Use Cases

• Automated Metrics: Utilize metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, and NDCG for quantitative assessment.
• Human Evaluation: Incorporate human judgment on dimensions such as accuracy, coherence, relevance, fluency, safety, and helpfulness.
• LLM-as-Judge: Leverage powerful LLMs to evaluate outputs, enabling scalable qualitative assessment.
• A/B Testing & Regression: Facilitate controlled experiments and continuous monitoring for performance regressions.
• Use Case: When deploying a new chatbot, use this Skill to systematically measure its response quality against a benchmark dataset using a combination of automated metrics and human review, ensuring it meets performance targets before going live.

Quick Start

Use the llm-eval skill to evaluate your model's accuracy and BLEU score against a set of test cases.