Behind the Prompts Evaluating LLMs Using Code
AI Summary
In this lecture, the focus is on code-graded evaluation as part of the AIQA engineering series. The presenter reviews the previous concepts of human-based and code-based evaluations, emphasizing the advantages of automating the evaluation process with code. Using Python and Jupyter Notebooks, the speaker demonstrates how to create evaluation datasets and generate prompts for large language models (LLMs) to obtain answers regarding the number of legs in various animals. The presentation includes practical coding examples and highlights the importance of clear prompting and accuracy in evaluations. The session concludes with a demonstration of how to improve prompting techniques to enhance response accuracy in LLMs, showcasing differences in performance with various models. Overall, the video serves as a practical guide for automating evaluations using code in AIQA engineering.