LLMs are basically an architecture of multiple Neural Networks (NNs), harnessing their abilities in Natural Language Processing (NLP) tasks. The NN of a LLM is called a transformer; a transformer has an encoder that deals with input sentences as a sequence of tokens and learns out relations between them based on their positioning, and a decoder that generates an output text based on what has been learned in the training phase; this is what we call a self-attention mechanism . Since the evolution of LLMs, many datasets were created; pre-training datasets, fine-tuning datasets, preference datasets, evaluation datasets, and traditional NLP datasets. In the case of utilizing LLMs for DD, our major drawback is the lack of comprehensive datasets for training .