Intro to LLM Evaluation w/ OpenAI Evals [Walk-Thru] скачать в хорошем качестве

Intro to LLM Evaluation w/ OpenAI Evals [Walk-Thru]

In this video, we explore the evolving landscape of large language models (LLMs) in 2025, particularly focusing on their adoption by larger enterprises. We delve into the critical aspects of AI implementation, such as monitoring, evaluation, and consistent performance. The video presents a four-step framework for evaluating LLMs, inspired by Lance Martin from Lane Chain. Additionally, we walk through a practical case study using a movie review dataset from Hugging Face to evaluate how well GPT-3.5 and GPT-4.0 models identify sentiment. This comprehensive guide covers everything from dataset preparation to setting evaluation criteria and comparing model performance, emphasizing the importance of regular and systematic evaluations for the successful deployment of LLMs. 00:00 Intro 01:17 4 -Part Framework 02:23 Double Click on LLMs 02:57 Tooling 03:28 Case Study: Movie Review 04:18 OpenAI Evaluation Walk Thru 07:38 Test Criteria 09:16 Test Evaluation 11:00 Run Evaluation 13:36 Closing ----- git repo: https://github.com/mannybernabe/opena... Sources: LangChain Eval Series:    • Why Evals Matter | LangSmith Evaluations -...   OpenAI Docs: https://platform.openai.com/docs/guid...