Summary
This article provides a comprehensive guide on G-Eval, a reference-free evaluation framework designed for assessing the quality of text generated by large language models (LLMs). It covers the mechanism of G-Eval, including rubric-based prompting, chain-of-thought evaluation, and probability-weighted scoring, along with tips on implementation and its limitations. Ideal for developers creating LLM applications, chatbots, and AI writing assistants.