Building Product Recommendation Logic Based on Customer Needs #S11E6

This is season eleven, episode six. In this episode, we will focus on how to train a custom GPT to recommend the right products based on customer needs. You will learn how to classify products by application, teach AI how to match customer requirements with the best options, and use structured decision-making models to improve AI-driven recommendations. By the end of this episode, you will know how to create an AI assistant that helps customers choose the right product, just like an experienced salesperson.

So far, we have trained AI to handle pricing and quotations. Now, we are moving into a more advanced task—helping customers select the right product based on their needs.

Let’s go step by step on how to classify products, define product selection rules, and train AI to provide personalized recommendations.

Step One: Categorizing Products by Application and Use Case

Before AI can recommend the best product, it needs a clear understanding of how products are grouped and which ones are best suited for different applications.

Most businesses sell products that can be categorized by features, intended users, and specific applications. For example:

If you sell electronics, products may be categorized by battery life, power output, or connectivity.

If you sell medical devices, categories may include patient type, use case, and compliance with regulations.

If you sell software, categories may focus on features, subscription levels, and integrations.

By grouping products into categories, AI can match customer questions with the right product based on key attributes.

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