Logbii's Deep Dive into LLM Evaluation Methods

Logbii's internal study group shares invaluable insights into evaluating the performance of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. The presentation by Matsuda, a full-stack AI engineer, offers a practical guide for those integrating LLMs into their products, providing a crucial framework for assessment.

• •The study group focuses on practical methods for evaluating LLMs and RAG.
• •Matsuda, a full-stack AI engineer, presented the evaluation methods.
• •The study emphasizes the importance of understanding the LLM evaluation process.