Overview
This Guidance demonstrates how generative AI technology can automate your product review and approval process for an e-commerce marketplace or retail website. It uses Amazon Bedrock, a fully managed service that offers a range of high-performing foundation models (FMs) with a broad set of capabilities you need to build generative AI applications. Here, it leverages computer vision and natural language processing to analyze product images, extract relevant attributes, and generate detailed product descriptions. Using the style guidelines of your website or marketplace, this Guidance can also be configured to develop descriptions from supplier-provided specifications and images, driving operational efficiency and improving your shopper's experience.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Well-Architected Pillars
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
Implementation Resources
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
Open sample code on GitHub
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
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