100% OFF- Build a Customer Support Agent using OpenAI and AzureML

This project aims to enhance customer support efficiency and reduce operational costs by leveraging Large Language Models (LLMs) and Azure Machine Learning for automated ticket categorization, prioritization, and response generation.

1. Introduction to AI-Powered Customer Support Automation

Begin with an overview of the challenges in managing large volumes of customer support tickets and the growing importance of automation. Understand how AI technologies like Azure ML and OpenAI can transform traditional customer support systems into intelligent, responsive agents.

2. Azure ML Workspace Setup and Data Analysis

Learn how to set up and configure your Azure ML workspace, connecting it seamlessly with your local development environment. You’ll then load and analyze a retail dataset containing customer support tickets to identify patterns and insights that will guide your model development.

3. LLM Integration and Vector Database Implementation

Integrate a pre-trained Large Language Model (LLM) to generate embeddings and responses. You’ll then set up a vector database using FAISS to store these embeddings efficiently, enabling fast and relevant retrieval of context-based information for customer queries.

4. Prompt Engineering and RAG Architecture

Master prompt engineering to design and refine input prompts that yield precise and contextually relevant responses. Implement the Retrieval-Augmented Generation (RAG) framework, combining retrieval-based and generative techniques to ensure your AI assistant responds intelligently using the stored vector data.

5. Response Generation, Sampling, and Feedback Loop

Develop robust response generation logic using the LLM and retrieved data. Implement response sampling to produce multiple candidate answers and select the most suitable one. Establish a feedback loop to continuously improve prompts and responses based on user interactions.

6. Streamlit UI Development and Azure Deployment

Focus on code modularity to maintain clarity and scalability. Build an interactive Streamlit interface to showcase your AI support agent’s capabilities. Finally, deploy your application on Azure ML, ensuring it operates efficiently, scales with demand, and remains easy to maintain in production environments.