Enhancing a Customer Support Chatbot - RAG and Fine Tuning

Introduction

In today’s fast-paced digital world, customer support chatbots play a crucial role in providing timely and accurate assistance to users. However, as products and services evolve, these chatbots often struggle to keep up with the latest information and handle complex queries effectively. This case study explores the challenges faced by a customer support team and how they can leverage advanced AI techniques to improve their chatbot’s performance.

Scenario

Alex and Jamie, two colleagues working in the customer support department of a tech company, are tasked with enhancing their chatbot’s capabilities. The chatbot has been underperforming, particularly with queries that require up-to-date information and a deep understanding of the company’s products and services. Alex and Jamie are considering two AI techniques: Retrieval Augmented Generation (RAG) and fine-tuning, but they are unsure which one to use.

Listen to the conversation between Alex and Jamie

Help Jamie to find answers for the below questions

Below are questions based on the scenario. Help Jamie identify when to use RAG, fine-tuning and both.

The chatbot needs to provide accurate responses about the latest software updates and features that were released last week. Which technique should be used?

RAG - Because the information is recent and needs to be retrieved from up-to-date sources.

The chatbot frequently encounters queries about troubleshooting common issues with the company's products, which have been consistent over the past year.Which technique should be used?

Fine-Tuning - Because the issues are consistent and can be addressed by training the model on a specific dataset.

The chatbot needs to answer questions about recent changes in company policies that are updated monthly.Which technique should be used?

RAG - Because the policies are updated frequently, and the chatbot needs to access the latest information.

The chatbot is required to handle a wide range of customer queries, including both frequently asked questions and the latest product announcements. Which technique should be used?

RAG and Fine-tuning - Because the chatbot needs to handle both consistent queries and the latest information.

Alex and Jamie decide to implement RAG to ensure their chatbot can provide the latest product information. They need a service to retrieve relevant documents from their knowledge base. Which Azure service should they use?

After fine-tuning the GPT model, Alex and Jamie want to deploy the chatbot. They need a service to manage this deployment. Which Azure service should they use?

Scenario: Alex and Jamie are implementing RAG for their customer support chatbot. They need to follow a sequence of steps to set it up correctly. Arrange the following steps in the correct order for implementing RAG:

Integrate the retrieved information with the user's query.
Deploy the RAG-enhanced chatbot.
Set up Azure AI Search to index the knowledge base.
Generate a response using the GPT model.
Retrieve relevant documents from the knowledge base.