This Guidance demonstrates how to streamline the process of extracting insights from customer feedback, enabling businesses to make data-driven decisions and enhance the overall customer experience. Manually analyzing large volumes of unstructured data like reviews and comments is time-consuming, prone to inconsistencies, and challenging to scale. Large language models (LLMs) available on Amazon Bedrock can efficiently categorize customer feedback, extract specific aspects, and determine associated sentiments, providing valuable insights into customer satisfaction levels and areas for improvement.

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Architecture Diagram

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Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.

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.

  • This Guidance uses Step Functions for efficient workflow orchestration, automating extract, transform, load (ETL) operations for customer feedback data. It employs modular Lambda functions, enabling easy maintenance. The end-to-end automation significantly reduces manual intervention, such as manually analyzing and categorizing large volumes of unstructured data, to minimize errors and improve consistency in feedback analysis.

    Read the Operational Excellence whitepaper 

  • The Guidance addresses security concerns when dealing with customer feedback data by implementing robust measures. It uses AWS KMS for encryption, Amazon S3 for secure data storage with fine-grained access controls, and a virtual private cloud (VPC) for network isolation. For LLM-powered insight extraction, it leverages Amazon Bedrock, which provides enterprise-grade security and privacy controls.

    Read the Security whitepaper 

  • Amazon S3, Lambda, Amazon RDS, QuickSight, and Amazon Bedrock significantly reduce operational overhead and improve system reliability by offloading infrastructure management to AWS. The Step Functions workflow includes comprehensive error handling and reliable state management, ensuring fault tolerance and process integrity. This Guidance also uses LLMs through Amazon Bedrock to consistently extract nuanced insights from unstructured data.

    Read the Reliability whitepaper 

  • Serverless components like Lambda and Step Functions enable automatic scaling to handle varying workloads. Step Functions has a map state processing mode for large-scale parallel workloads, allowing for efficient processing of extensive datasets. For data analytics and visualization, this Guidance integrates QuickSight, which uses its in-memory computation engine (SPICE) to provide fast query performance on large datasets. The integration of LLMs through Amazon Bedrock significantly boosts natural language processing capabilities, leading to more accurate insight extraction.

    Read the Performance Efficiency whitepaper 

  • The serverless architecture, through services like Lambda and Step Functions, help ensure that costs are directly tied to actual usage, preventing overprovisioning and unnecessary expenses. Storage costs are optimized through the use of Amazon S3 for cost-effective storage of input files and processed data. Efficiently processing and categorizing feedback also contributes to cost savings by reducing the need for manual analysis and enabling more targeted use of human resources. For data visualization, QuickSight allows you to optimize costs based on your usage patterns, as you pay only for the amount of resources used.

    Read the Cost Optimization whitepaper 

  • Serverless scaling minimizes energy consumption. Amazon S3 and Amazon RDS optimize resource utilization, and the integration of Amazon Bedrock reduces the need for energy-intensive model training. You can further enhance sustainability by monitoring resource usage, implementing data lifecycle policies, and optimizing Lambda functions and Step Functions workflows.

    Read the Sustainability whitepaper 

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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.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.

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