Analysis and Evaluation of Multiple Prompt Engineering Techniques to Enhance the Performance of Large Language Model-Based Chatbots

Large Language Models (LLMs), with their advanced natural language processing capabilities, have facilitated the development of intelligent chatbots; however, their performance heavily relies on the quality of input prompts, and prior methods have faced challenges in delivering step-by-step reasoning and interpretable responses. LLMs are a category of artificial intelligence models trained on vast corpora of text to acquire the capability of understanding and generating human language. These models can generate text, answer questions, summarize, translate, perform reasoning, and even write code. LLMs operate based on the Transformer architecture, learning to predict linguistic patterns, semantics, context, and semantic relationships between words. As the volume of training data and the number of parameters increase, the model becomes capable of providing more accurate, natural, and human-like responses. Today, these models play a pivotal role in chatbots, search engines, educational systems, data analytics, and numerous other domains. In this study, by integrating the Chain-of-Thought (CoT) technique into multiple prompt engineering, a novel technical framework is proposed. This framework successfully improves chatbot performance in addressing complex queries by enhancing accuracy, reliability, and reasoning transparency.