Automating Product Descriptions Using Generative AI

Solution Overview

In this solution, you will learn how to build a generative AI-powered architecture that processes images using advanced vision models, generating accurate and compelling descriptions of an image. MongoDB Atlas serves as the operational data layer, leveraging its flexible data model to scale seamlessly as new descriptions are added and ensuring efficient data management and effortless scalability.

This solution proves particularly valuable in the retail industry. The process of onboarding a new product to a retailer’s catalog can be time-consuming, especially when it comes to crafting product descriptions—in multiple languages and in different geographies. This solution helps streamline that workflow by automating the initial content creation through generative AI, providing retailers with a solid foundation for each product description, ultimately speeding up the time to market and improving consistency across their catalog UX writing.

Reference Architectures

With MongoDB:

At the core of this architecture we have three key components:

• MongoDB Atlas: A powerful, general-purpose data platform that seamlessly manages our data in the cloud. MongoDB's flexible document model allows products to scale easily by adding more descriptions (i.e. for translations in multiple languages) without introducing complexity.

• The object storage: An effective system for efficient product image file storage and retrieval. This can be built with Google Cloud storage, Azure Blob Storage or AWS S3.

• Together AI: Offers a variety of generative AI services, making it easy to run or fine-tune leading open source models with only a few lines of code. This solution uses their available vision LLMs to generate the product descriptions.

Figure 1. Product description generative AI architecture

The journey starts on the left side of the diagram with the User/Event label. A laptop icon represents the “Product Description Generator” system. First, a new product is received, this can be added manually by a user or in bulk through an automated event.

Then, retailers generate the descriptions by sending a query to Together AI’s endpoint, utilizing their vision models, combining computer vision and natural language processing (NLP) to process and understand images alongside text. The request includes the image URL, the desired description length, the vision model utilized, and the languages for the description.

Then, Together AI takes this data and uses one of its Llama3 vision models to scan the image and generate a description that matches the specified requirements, and returns the product description to the application.

Finally, the product along with its description will be upserted inside the catalog on MongoDB, ensuring real-time availability across all connected systems.

To illustrate the scalability of this architecture, consider the diagram below. By integrating MongoDB Change Streams, it allows real-time updates on any application listening to the catalog. For example, the e-commerce portal, a social media platform, and any other touchpoints.

Figure 2. Product description generative AI architecture with real-time applications

Building the Solution

Building this solution can be broken down in five major steps:

For complete implementation details, including code samples, configuration files, and tutorial videos, visit the GitHub repository.

Key Learnings

• High-quality product descriptions drive business success: A well-crafted product description enhances user engagement, improves SEO rankings with increased views, and helps customers make more informed purchase decisions.

• Leverage MongoDB and Together AI to automate product descriptions: By combining MongoDB’s flexible and scalable database with Together AI’s available vision models, retailers are able to automate real-time product description generation that aligns with their business needs.

• Modern architectures speed up time to market: Streamlining the product onboarding process with AI and automation reduces manual effort and speeds up approvals. Using a generative AI for product descriptions maintains consistent UX and tone, enabling quick scalability for expanding product catalogs with little extra effort.

Together, these capabilities form a robust generative AI system that scales with your business needs while ensuring efficiency and reliability.

Technologies and Products Used

Authors

• Angie Guemes, MongoDB

• Prashant Juttukonda, MongoDB