GenAI For Business Analysis: Fine-Tuning LLMs

In this 2-hour project, you'll learn how to fine-tune the GPT-3.5 model using the OpenAI API in Python. You are an AI engineer employed by PulseNet, a telecommunications company that provides internet, television, and phone services. PulseNet operates with a large customer base and manages a substantial volume of daily inquiries, support requests, and product reviews. The company has received numerous complaints from customers, expressing dissatisfaction. PulseNet's objective is to enhance customer satisfaction by analyzing customer complaints more regularly to address and fix issues regarding their services. They require a Large Language model capable of extracting specific details from each complaint, including the topic, problem, and customer dissatisfaction index in real-time. This dissatisfaction index will range between 0 and 100, representing the level of customer anger derived from the complaint text. PulseNet has provided a dataset containing the latest 50 user complaints along with the extracted information in the desired format. Your role as an AI engineer is to use the OpenAI API and Python to fine-tune the GPT-3.5 model and retrain a new large language model (LLM) that is capable of extracting the necessary information from a given customer complaint in the desired format. To get the most out of this course, you'll need access to the OpenAI API Key and a basic understanding of data analysis concepts, including data types, and data manipulation, along with some familiarity with Python. This course is for those who are experienced data analysts with at least a basic knowledge of Python and want to explore the exciting applications of generative AI in data analysis.

• Project Overview
• In this 2-hour project, you'll learn how to fine-tune the GPT-3.5 model using the OpenAI API in Python. You are an AI engineer employed by PulseNet, a telecommunications company that provides internet, television, and phone services. PulseNet operates with a large customer base and manages a substantial volume of daily inquiries, support requests, and product reviews. The company has received numerous complaints from customers, expressing dissatisfaction. PulseNet's objective is to enhance customer satisfaction by analyzing customer complaints more regularly to address and fix issues regarding their services. They require a Large Language model capable of extracting specific details from each complaint, including the topic, problem, and customer dissatisfaction index in real-time. This dissatisfaction index will range between 0 and 100, representing the level of customer anger derived from the complaint text. PulseNet has provided a dataset containing the latest 50 user complaints along with the extracted information in the desired format. Your role as an AI engineer is to use the OpenAI API and Python to fine-tune the GPT-3.5 model and retrain a new large language model (LLM) that is capable of extracting the necessary information from a given customer complaint in the desired format. To get the most out of this course, you'll need access to the OpenAI API Key and a basic understanding of data analysis concepts, including data types, data manipulation, along with some familiarity with Python. This course is for those who are experienced data analysts with at least a basic knowledge in Python and want to explore the exciting applications of generative AI in data analysis.