Building on the foundation laid in the first part, “Selecting and upgrading models using Evaluations – Part 2” delves deeper into the AI model evaluation process. This piece covers how to leverage more advanced models to assess and improve less capable ones, enhancing model performance by systematically testing through evaluations. In this insightful continuation, the AI Toolkit for Visual Studio Code extension plays a pivotal role, enabling users to conduct sophisticated evaluations using a “bulk-run” feature that automates parts of the manual assessment process. Whether comparing older model versions with newer ones or assessing fine-tuned models against large frameworks like GPT-4o, the article emphasizes the significance of selecting the right evaluation metrics.
The article outlines several key evaluators – coherence, fluency, relevance, similarity, BLEU, F1 Score, GLEU, and METEOR – each designed to test specific performance aspects of AI models. By guiding readers on how to harness these evaluators using Visual Studio Code, it provides a practical approach to enhancing AI model efficacy. Moreover, it underscores the need for a blend of automated evaluations and human review to ensure comprehensive analysis, especially in critical domains. The article encourages iterative testing and leveraging data insights for subsequent AI application improvements.
News: Selecting and upgrading models using Evaluations – Part 2
Documentation: Docs for AI toolkit
