In the ever-evolving landscape of Artificial Intelligence (AI), the development and deployment of Large Language Models (LLMs) have become pivotal in shaping intelligent applications across various domains. However, realizing this potential requires a rigorous and systematic evaluation process. Before delving into the metrics and challenges associated with evaluating LLM systems, let’s pause for a moment to consider the current approach to evaluation. Does your evaluation process resemble the repetitive loop of running LLM applications on a list of prompts, manually inspecting outputs, and attempting to gauge quality based on each input? If so, it’s time to recognize that evaluation is not a one-time endeavor but a multi-step, iterative process that has a significant impact on the performance and longevity of your LLM application. With the rise of LLMOps (an extension of MLOps tailored for Large Language Models), the integration of CI/CE/CD (Continuous Integration/Continuous Evaluation/Continuous Deployment) has become indispensable for effectively overseeing the lifecycle of applications powered by LLMs. — Read More