Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
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The llm-evaluation skill is designed to implement comprehensive evaluation strategies specifically tailored for Large Language Model (LLM) applications. It addresses the need for systematic and effective methods to measure and enhance the performance of LLMs. By leveraging automated metrics, human feedback, and benchmarking techniques, this skill provides users with a robust framework for assessing the quality of AI applications, ensuring that LLMs function optimally in diverse scenarios.
You can begin by importing the EvaluationSuite class and defining the metrics you want to evaluate. Then, create test cases to assess your model.
The skill supports various automated metrics such as BLEU, ROUGE, METEOR, and more for text generation, as well as classification and retrieval metrics.
Yes, you can create custom metrics by defining a name and a function that calculates the desired score.
The skill is implemented in Python and requires relevant libraries like NumPy and NLTK for metric calculations.
The skill offers automated metrics, human evaluation, and LLM-as-Judge approaches for a comprehensive evaluation.
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wshobson/agents