Quality Engineering: Moving Beyond Luck to Measure Your AI Agents [Part 2]

In this continuation of our exploration into AI quality assessment, we delve into two essential metrics that can significantly enhance the evaluation process. The first metric offers a universal approach to determine the accuracy of responses generated by large language models (LLMs). The second metric is specifically tailored for evaluating text summarization tasks. We will illustrate these concepts using the RAGAS library, providing insights into the inner workings of these metrics. By implementing these measurement techniques, developers can move beyond mere hope and gain a clearer understanding of their AI agents' performance. This shift not only improves the reliability of AI outputs but also fosters a more structured approach to AI development. As we dissect the functionality of these metrics, we aim to equip practitioners with the tools necessary for meaningful assessment and enhancement of AI systems.