Summary
In this chapter, we outlined critical strategies for evaluating LLM applications, ensuring robust performance before production deployment. We provided an overview of the importance of evaluation, architectural challenges, evaluation strategies, and types of evaluation. We then demonstrated practical evaluation techniques through code examples, including correctness evaluation using exact matches and LLM-as-a-judge approaches. For instance, we showed how to implement the ExactMatchStringEvaluator for comparing answers about Federal Reserve interest rates, and how to use ScoreStringEvalChain for more nuanced evaluations. The examples also covered JSON format validation using JsonValidityEvaluator and assessment of agent trajectories in healthcare scenarios.
Tools like LangChain provide predefined evaluators for criteria such as conciseness and relevance, while platforms like LangSmith enable comprehensive testing and monitoring. The chapter presented code examples using LangSmith...
