Evaluation and Testing
As we’ve discussed so far in this book, LLM agents and systems have diverse applications across industries. However, taking these complex neural network systems from research to real-world deployment comes with significant challenges and necessitates robust evaluation strategies and testing methodologies.
Evaluating LLM agents and apps in LangChain comes with new methods and metrics that can help ensure optimized, reliable, and ethically sound outcomes. This chapter delves into the intricacies of evaluating LLM agents, covering system-level evaluation, evaluation-driven design, offline and online evaluation methods, and practical examples with Python code.
By the end of this chapter, you will have a comprehensive understanding of how to evaluate LLM agents and ensure their alignment with intended goals and governance requirements. In all, this chapter will cover:
- Why evaluations matter
- What we evaluate: core agent capabilities
- How...
