Abstract
Chat-GPT has become increasingly popular and has provided tremendous help to people in their daily life. The fundamental working principle of Chat-GPT involves the incorporation of various methods such as Natural Language Processing (NLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Transformer and Reinforcement Learning from Human Feedback model (RLHF). These methods assist Chat-GPT in understanding, predicting and computing the desired outcomes for the users. NLP is used to help machines understand and process human language. RNN is employed to facilitate the machine in comprehending the input's logic. LSTM enables the control of memory elements, allowing the machine to combine unrelated elements in memory. RLHF serves as a switch button, controlling the machine's output and improving the accuracy of results. Chat-GPT can be used for image editing, coding, translation, paper editing and other tasks. However, it is critical to exercise caution while using Chat-GPT and not to become overly reliant on it, given the potential problems it may create.
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