Artificial Intelligence

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Reference architecture for summarizing customer reviews using Amazon Bedrock

Analyze customer reviews using Amazon Bedrock

This post explores an innovative application of large language models (LLMs) to automate the process of customer review analysis. LLMs are a type of foundation model (FM) that have been pre-trained on vast amounts of text data. This post discusses how LLMs can be accessed through Amazon Bedrock to build a generative AI solution that automatically summarizes key information, recognizes the customer sentiment, and generates actionable insights from customer reviews. This method shows significant promise in saving human analysts time while producing high-quality results. We examine the approach in detail, provide examples, highlight key benefits and limitations, and discuss future opportunities for more advanced product review summarization through generative AI.

solution-architecture-accuracy

Accuracy evaluation framework for Amazon Q Business

Generative artificial intelligence (AI), particularly Retrieval Augmented Generation (RAG) solutions, are rapidly demonstrating their vast potential to revolutionize enterprise operations. RAG models combine the strengths of information retrieval systems with advanced natural language generation, enabling more contextually accurate and informative outputs. From automating customer interactions to optimizing backend operation processes, these technologies are not just […]

Accelerate performance using a custom chunking mechanism with Amazon Bedrock

This post explores how Accenture used the customization capabilities of Knowledge Bases for Amazon Bedrock to incorporate their data processing workflow and custom logic to create a custom chunking mechanism that enhances the performance of Retrieval Augmented Generation (RAG) and unlock the potential of your PDF data.

Use IP-restricted presigned URLs to enhance security in Amazon SageMaker Ground Truth

While presigned URLs offer a convenient way to grant temporary access to S3 objects, sharing these URLs with people outside of the workteam can lead to unintended access of those objects. To mitigate this risk and enhance the security of SageMaker Ground Truth labeling tasks, we have introduced a new feature that adds an additional layer of security by restricting access to the presigned URLs to the worker’s IP address or virtual private cloud (VPC) endpoint from which they access the labeling task. In this blog post, we show you how to enable this feature, allowing you to enhance your data security as needed, and outline the success criteria for this feature, including the scenarios where it will be most beneficial.

Unlock the power of structured data for enterprises using natural language with Amazon Q Business

In this post, we discuss an architecture to query structured data using Amazon Q Business, and build out an application to query cost and usage data in Amazon Athena with Amazon Q Business. Amazon Q Business can create SQL queries to your data sources when provided with the database schema, additional metadata describing the columns and tables, and prompting instructions. You can extend this architecture to use additional data sources, query validation, and prompting techniques to cover a wider range of use cases.

Introducing document-level sync reports: Enhanced data sync visibility in Amazon Q Business

Amazon Q Business is a fully managed, generative artificial intelligence (AI)-powered assistant that helps enterprises unlock the value of their data and knowledge. With Amazon Q, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise […]

Derive generative AI-powered insights from ServiceNow with Amazon Q Business

This post shows how to configure the Amazon Q ServiceNow connector to index your ServiceNow platform and take advantage of generative AI searches in Amazon Q. We use an example of an illustrative ServiceNow platform to discuss technical topics related to AWS services.

Harness the power of AI and ML using Splunk and Amazon SageMaker Canvas

For organizations looking beyond the use of out-of-the-box Splunk AI/ML features, this post explores how Amazon SageMaker Canvas, a no-code ML development service, can be used in conjunction with data collected in Splunk to drive actionable insights. We also demonstrate how to use the generative AI capabilities of SageMaker Canvas to speed up your data exploration and help you build better ML models.

How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

This post provides an overview of a custom solution developed by the AWS Generative AI Innovation Center (GenAIIC) for Deltek, a globally recognized standard for project-based businesses in both government contracting and professional services. Deltek serves over 30,000 clients with industry-specific software and information solutions. In this collaboration, the AWS GenAIIC team created a RAG-based solution for Deltek to enable Q&A on single and multiple government solicitation documents. The solution uses AWS services including Amazon Textract, Amazon OpenSearch Service, and Amazon Bedrock.

Cisco achieves 50% latency improvement using Amazon SageMaker Inference faster autoscaling feature

Webex by Cisco is a leading provider of cloud-based collaboration solutions which includes video meetings, calling, messaging, events, polling, asynchronous video and customer experience solutions like contact center and purpose-built collaboration devices. Webex’s focus on delivering inclusive collaboration experiences fuels our innovation, which leverages AI and Machine Learning, to remove the barriers of geography, language, personality, and familiarity with technology. Its solutions are underpinned with security and privacy by design. Webex works with the world’s leading business and productivity apps – including AWS. This blog post highlights how Cisco implemented faster autoscaling release reference.