Problem

Our team was tasked with creating a chatbot that could understand and respond to user inquiries in a way that felt natural and human-like. However, we quickly realized that traditional rule-based chatbots were not sufficient for this task, as they often struggled to understand the nuances of language and context.

We needed a solution that could accurately interpret user messages and provide relevant responses, without relying on rigid pre-programmed rules.

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Solution

After extensive research and experimentation, we decided to implement a RAG (Red-Amber-Green) based chatbot using GPT (Generative Pre-trained Transformer) technology. This allowed us to create a more flexible and dynamic chatbot that could learn from user interactions over time.

The RAG system allowed the bot to categorize incoming messages based on their level of urgency or importance, while GPT technology enabled it to generate responses based on contextual understanding rather than simple keyword matching.

Impact

Increased customer satisfaction by X%
Reduced response time by X%

"Quote of how incredible of an impact this had."

Team

Team Member #1 - Machine Learning Engineer
- Built the RAG classification system
- Tuned the GPT model for optimal performance
Team Member #2 - UX Designer
- Designed the chatbot interface and user flow
- Conducted user testing and feedback sessions

RAG-Based GPT Chatbot Boosts Customer Satisfaction