Building applications with Large Language Models introduces unique challenges when it comes to verifying their behavior and performance. Unlike traditional software where outputs are often deterministic, LLM responses can vary, making standard testing approaches insufficient on their own. Assessing the quality and reliability of LLM-generated content requires specific methods and considerations.

This chapter addresses the practical aspects of testing and evaluating the Python-based LLM applications you've learned to build. We will cover:

• The distinct difficulties encountered when testing systems driven by LLMs.
• Techniques for unit testing specific components, such as prompt templates and output parsers.
• Strategies for integration testing entire LLM workflows.
• Methods for evaluating application performance, including relevant metrics and the role of human feedback.
• An introduction to frameworks designed to aid in LLM evaluation.
• Best practices for logging and monitoring interactions within your LLM applications.

By the end of this chapter, you will understand how to implement structured testing processes and evaluation techniques tailored to the specific nature of LLM-powered systems.