Comprehensive Framework for Quality Assurance of Generative AI Text

This paper presents a comprehensive framework for the quality assurance (QA) of text outputs generated by artificial intelligence (AI) models. The framework incorporates multiple metrics to evaluate the generated text, including grammar and spelling correctness, relevance to the prompt, and linguistic diversity. The proposed method employs the Python library language for grammatical error detection, TF-IDF vectorization coupled with cosine similarity for relevance assessment, and NLTK for measuring lexical diversity. By integrating these metrics, the framework provides a robust mechanism to ensure the generated text meets the desired quality standards. This approach is demonstrated through a sample implementation in Python, which can be easily extended and customized for various applications in generative AI.