Validation of Image Content Using LLMs and RAGs: A Strategy for Ensuring Compliance in Software Testing

This article presents an approach for validating content in images using large language models (LLMs) and retrieval-augmented generation (RAGs) as essential tools. The proposal aims to enhance software testing processes by ensuring compliance and accuracy of visual data used. Through the integration of these technologies, the study demonstrates how it is possible to automate content verification in images, identifying inconsistencies and ensuring that data meet established standards. Additionally, the challenges and limitations of this approach, as well as its practical applications in software testing scenarios, are discussed. The results indicate that the combination of LLMs and RAGs offers an efficient and scalable solution for visual content validation, significantly contributing to the quality and reliability of testing processes.