The integration of linguistic pattern matching and Large Language Model (LLM) solutions offers a comprehensive approach to enhancing chatbot applications. While LLMs provide advanced capabilities, they pose challenges such as response quality issues and processing costs. However, employing linguistic pattern-matching rules in advanced chatbots ensures transparency, controllability, and tunability, reducing hallucination effects and processing costs. Combining linguistic rules-based solutions with LLM-driven approaches, utilizing a Vector Database and Similarity Search, proves advantageous in scenarios requiring both common user queries and long-tail questions. This integrated approach stores product data in the Vector Database clarifies user queries using linguistic rules, and uses LLM-driven solutions for structured data retrieval. Gradually incorporating common questions into linguistic rule-based solutions enhances cost efficiency and response accuracy over time, delivering tailored responses that adapt to various user queries and preferences, thereby enhancing the overall user experience.


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