Navigating the World of Chatbots with LLM Evaluation: A Databricks Case Study

<p>Hello&hellip;there! Chatbots have become an integral part of our digital interactions, and they owe their prowess to Large Language Models (LLMs). One cutting-edge approach in chatbot development is the Retrieval Augmented Generation (RAG) architecture.</p> <blockquote> <p>It combines the best of both worlds: knowledge bases and generative models, offering reduced hallucinations, up-to-date information, and domain-specific knowledge.</p> </blockquote> <p><img alt="" src="https://miro.medium.com/v2/resize:fit:640/1*iwpL0OKuZ4lyEMYZFPeqwA.jpeg" style="height:420px; width:640px" /></p> <p>Graphics Credits:&nbsp;<a href="https://www.google.com/url?sa=i&amp;url=https%3A%2F%2Fpixabay.com%2Fimages%2Fsearch%2Fchatbot%2F&amp;psig=AOvVaw1x9KAdoPSuk9IZ9LLKOXiD&amp;ust=1694933122060000&amp;cd=vfe&amp;opi=89978449&amp;ved=0CA8QjRxqFwoTCOC5o5XEroEDFQAAAAAdAAAAABAD" rel="noopener ugc nofollow" target="_blank">Pixababy</a></p> <blockquote> <p>However, evaluating chatbot responses generated by these models has proven to be quite the puzzle. Human grading, while reliable, is labor-intensive and tough to scale.</p> </blockquote> <p><em>But fear not!</em></p> <p><strong>Databricks, in collaboration with Quinn Leng, Senior Software Engineer, has embarked on a mission to shed light on LLM automated evaluation best practices.</strong></p> <h2>Let me take you through their fascinating journey, focusing on the Databricks Documentation Bot.</h2> <p><a href="https://jasminbharadiya.medium.com/navigating-the-world-of-chatbots-with-llm-evaluation-a-databricks-case-study-efed941501e4"><strong>Website</strong></a></p>