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Solution overview This section outlines the architecture designed for an email support system using generative AI. High Level SystemDesign The solution consists of the following components: Email service – This component manages incoming and outgoing customer emails, serving as the primary interface for email communications.
The solution has been designed using the following services: Amazon Elastic Container Service (ECS) : to deploy and manage our Streamlit UI. Amazon Lambda : to run the backend code, which encompasses the generative logic. In step 5, the lambda function triggers the Amazon Textract to parse and extract data from pdf documents.
Ground truth data in AI refers to data that is known to be factual, representing the expected use case outcome for the system being modeled. By providing an expected outcome to measure against, ground truth data unlocks the ability to deterministically evaluate system quality. .
Now that you understand the concepts for semantic and hierarchical chunking, in case you want to have more flexibility, you can use a Lambda function for adding custom processing logic to chunks such as metadata processing or defining your custom logic for chunking. Make sure to create the Lambda layer for the specific open source framework.
The agent can recommend software and architecture design best practices using the AWS Well-Architected Framework for the overall systemdesign. Create and associate an action group with an API schema and a Lambda function. Recommend AWS best practices for systemdesign with the AWS Well-Architected Framework guidelines.
Integration with AWS Services: AWS Batch seamlessly integrates with other AWS services, such as Amazon S3, AWS Lambda, and Amazon DynamoDB. Each job references a job definition. It’s built on serverless services (API Gateway / Lambda) and provides the same functionality as the CLI tool pcluster.
Consider the following systemdesign and optimization techniques: Architectural considerations : Multi-stage prompting – Use initial prompts for data retrieval, followed by specific prompts for summary generation. For example, “Cross-reference generated figures with golden source business data.” Don’t make up any statistics.”
To set up SageMaker Studio, refer to Launch Amazon SageMaker Studio. Refer to the SageMaker JupyterLab documentation to set up and launch a JupyterLab notebook. For more details, refer to Evaluate Bedrock Imported Models. He specializes in generative AI, artificial intelligence, machine learning, and systemdesign.
“Build one to throw away” shouldn’t refer to your flagship product. Allow yourself time to vet and review references. Good examples are AWS Lambda or Cloudflare Workers. Use a DesignSystem. Good leadership will create a sense of ownership. Invest in your team; train the team early in the process.
Based on the answer to these questions, Amazon introduced a service called Lambda in 2014 that responds to events quickly and inexpensively. Lambda replaced the need for customers to pay for servers sitting around listening for events to occur – reducing the cost (and Amazon’s revenue) for event-driven systems by a factor of 5 to 10 (!).
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