About the Project
Project Overview
Developed as a learning project, the Hotel Query Response System utilizes advanced NLP techniques to enhance email customer support. The system processes queries using a sophisticated Retriever-Generator (RAG) architecture, extracting and utilizing data efficiently to deliver responses that are aligned with the hotel's brand tone. This project was designed to both provide a practical solution to real-world problems and serve as a comprehensive learning experience in applying NLP in the hospitality industry.
Skills Used
- Natural Language Processing
- Python
- Machine Learning
- Data Retrieval Systems
- FAISS for Efficient Similarity Search
Problem Identification
The project addresses the challenge of efficiently managing high volumes of customer queries through email in the hotel industry, aiming to improve response times and customer satisfaction.
Mission
The mission was to apply theoretical NLP concepts practically to develop a system that automates and optimizes customer service processes, enhancing learning and operational efficiency.
Challenges Faced
The challenge involved integrating NLP to accurately understand and respond to diverse and complex customer inquiries, ensuring the responses are both appropriate and timely.
Outcome
The system has performed as expected, providing a significant learning experience and yielding good results in automating responses, thus demonstrating the project's success in applying AI to real-world customer service challenges.