Episode 10: The Judge – The Art and Science of Evaluating LLM Applications
The final, but perhaps most important step: evaluation. What are we actually testing? Dive into offline evaluation with example suites, how to find samples, evaluating solutions (including SOMA assessment), and online evaluation through A/B testing and various metrics. Learn to ensure quality and effectiveness in your LLM projects.
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Transkript
(Narrator): Welcome to Chapter 10: Evaluating LLMs. This chapter is part of Part III of the book, focusing on advanced techniques. After discussing how to build conversational agents in Chapter 8 and LLM workflows in Chapter 9, Chapter 10 addresses the crucial aspect of ensuring their quality and effectiveness through evaluation.
(Narrator): While the full text of this chapter wasn't provided in the sources, mentions in other parts of the book indicate its core topics.
(Narrator): Evaluating LLMs is essential for determining how well your applications and the underlying models are performing. The chapter title itself highlights this focus on evaluation.
(Narrator): Key areas covered in Chapter 10 include using metrics to quantify performance. It also explores methods for testing and refining various parameters and approaches.
(Narrator): Practical testing strategies are discussed, such as using offline harness tests before production. For assessing performance in real-world scenarios, the chapter covers live-traffic A/B tests.
(Narrator): Functional testing is another technique mentioned, which involves confirming that certain aspects of the completion "work". An example given is checking if the correct tool is called with the right syntax.
(Narrator): The importance of recording input/output data from real traffic is also noted, which can be sampled to check for quality degradation or used to evaluate competing implementations in A/B tests.
(Narrator): In essence, Chapter 10 equips you with the techniques to measure, test, and refine your LLM applications to ensure they deliver high-quality results.