Personalised Assistance
Navigating websites and locating the right information can be challenging. Museum wanted to make it easier for the users to access personalized information for a better visitor experience. A LLM based solution was designed to address the issue and further evaluate with the end users for validation.
Before : User's POV
Overview
Improve visitor's access to information for better museum experience leading to greater engagement and satisfaction.
I built a llm based chatbot prototype to help the museum administration validate the use case so that the administration can create business case to implement llm based chatbot for their online customer enquiries and concierge.
I used RAG based architecture for LLM to process user query, search a vector database for relevant information and return responses in predefined formats using user's input language.
Website Scraping
Vector Embeddings
Language Detection
Vector Search
Content Filtration
Template Matching
Response Generation
RAG Search
Suggested Questions
Context Preservation
Follow Up Questions
Multilingual
As per the usability test, all 24 users from Hong Kong and Mainland China used chatbot in different languages (English, Traditional Chinese and Simplified Chinese),and rated their experience with chatbot higher compared to their experience with the website.
This helped the museum administration to validate their hypotheses and build business case to get their project sponsored for LLM based chatbot.