This is season eleven, episode two. In this episode, we will focus on how to collect and structure past customer replies to train a custom GPT. You will learn how to gather historical email responses, identify common patterns, clean the data, and organize it into a structured format that an AI model can use. By the end of this episode, you will have a clear understanding of how to prepare your customer support data for automation. If you want your custom GPT to generate accurate and helpful responses, it needs a strong foundation of real-world data. AI learns best when it has examples to reference. If your business has been handling customer inquiries for a while, you already have valuable training material in the form of emails, chat logs, and past responses. Instead of starting from scratch, you can use this data to make your AI assistant more effective from the beginning. Let’s go through the step-by-step process of preparing this data for training a custom GPT. Step One: Collecting Past Customer Replies The first step is to gather all existing customer interactions. These could be: Emails from customers and your replies Live chat logs from customer support systems Frequently asked questions and answers from your website Internal documents with product explanations or troubleshooting guides To start, go through your email inbox and export past customer conversations. If you use a customer s...

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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