This article describes a new methodological procedure for testing and verifying the authenticity of prudent investors (PIs) responses using a dataset. The approach consists of several stages, from intensively examining the dataset to establishing an evaluation model. Llama3 AI is used in this process, which starts by simulating conversations to generate relevant answers together with AI-created real-life situations. Evaluation relies on Jaccard and cosine similarity (JCS) metrics that eventually compute truth measures for response validity. The truth measure is calculated using Jaccard and cosine similarity measures to determine whether responses are genuine. This method makes clear investment decision-making processes by blending AI-originating content with real cases that are usually complex. This all-around technique tries to make sense of answers, giving a deep understanding of the intricacies involved in making investment decisions. Artificial intelligence and real-life scenarios change the way we make investment decisions. This inventive technique implies that responses will be more dependable and that we will better understand what goes into making an investment choice.