Скачать или смотреть Taming AI The Unseen World of LLM Evaluation

The provided text, a transcript from a YouTube video by Taylor Jordan Smith titled "Strategies for LLM Evals," outlines the *critical need for robust evaluation and benchmarking strategies when deploying Large Language Models (LLMs) in production environments**. It emphasizes that **setting up generative AI for scalability, reliability, and safety is challenging**, citing drawbacks like policy restrictions, legal risks, bias, cost, performance issues, and knowledge cut-offs. The presentation differentiates **evaluation* (a comprehensive model assessment) from *benchmarking* (comparing models against specific tasks and datasets), explaining a tiered approach to evaluation, from *system performance to factual accuracy, safety, and bias mitigation**. Hands-on activities are discussed, demonstrating tools like **GuideLLM for system performance benchmarking* and **ML-Eval-Harness for factual accuracy (MMLU Pro)**, concluding with **Prompt Fu for custom safety evaluations**, all highlighting the importance of **continuous improvement through CI/CD integration**.