Using generative AI with DynamoDB - Amazon DynamoDB
Generative AI use cases for DynamoDBGenerative AI blogs for DynamoDB

Using generative AI with DynamoDB

Amazon DynamoDB is a serverless, NoSQL, fully managed database with single-digit millisecond performance at any scale. DynamoDB is optimized for high-throughput workloads and you can extend its capabilities by integrating with generative AI models. Using generative AI models, you can work with data stored in DynamoDB tables in real-time and build applications that are contextually aware and highly personalized. You can also enhance the end user experience by fully leveraging your business, user, and application data to customize your generative AI solutions.

For more information about gen AI and the solutions AWS provides to build gen AI applications, see Transform your business with generative AI.

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Generative AI use cases for DynamoDB

DynamoDB is widely used in AI powered conversational applications, such as chatbots and call centers built with a Foundation Model (FM). You can access FMs through Amazon Bedrock, Amazon SageMaker AI, or other model providers. Such applications commonly use DynamoDB to improve personalization and enhance the user experience across three data patterns: application data, business data, and user data. Some examples of these data patterns are as follows:

  • Storage of application data, such as chat message history, through integrations with LangChain, LlamaIndex, or a custom code. This context enhances the user experience by allowing the model to converse back and forth with the user.

  • Creation of a customized user experience by leveraging business data, such as inventory, pricing, and documentation.

  • Application of user data, such as web history, past orders, and user preferences, to provide personalized answers.

For instance, an insurance company can build a chatbot using DynamoDB to provide their Retrieval-Augmented Generation (RAG) based gen AI model access to near real-time data. Examples of such data are real-time mortgage rates, product pricing, compliant/standard contract copy, user web history, and user preferences. Combining DynamoDB with RAG adds in-depth and updated information about insurance products and the user data. This enriches the prompts and answers to provide end users with an accurate, personalized, and near real-time experience.

Similarly, financial services industry customers use DynamoDB, Amazon Bedrock knowledge bases, and Amazon Bedrock agents to build RAG-based gen AI applications. These applications can use open-source earnings reports and call transcripts. They can also use user-specific portfolio and transaction history to generate an on-demand summary of portfolio including an outlook for the future.

Generative AI blogs for DynamoDB

The following articles offer detailed use cases, best practices, and step-by-step guides to help you leverage DynamoDB's capabilities in building advanced AI-powered applications.