Category: Amazon Bedrock

In this blog post, we will share some of capabilities to help you get quick and easy visibility into Amazon Bedrock workloads in context of your broader application. We will use the contextual conversational assistant example in the Amazon Bedrock GitHub repository to provide examples of how you can customize these views to further enhance visibility, tailored to your use case. Specifically, we will describe how you can use the new automatic dashboard in Amazon CloudWatch to get a single pane of glass visibility into the usage and performance of Amazon Bedrock models and gain end-to-end visibility by customizing dashboards with widgets that provide visibility and insights into components and operations such as Retrieval Augmented Generation in your application.

This post is a continuation of Creating Natural Conversations with Amazon Lex QnAIntent and Amazon Bedrock Knowledge Base. In summary, we explored new capabilities available through Amazon Lex QnAIntent, powered by Amazon Bedrock, that enable you to harness natural language understanding and your own knowledge repositories to provide real-time, conversational experiences. In many cases, Amazon […]

This is a guest post co-written with Ori Nakar from Imperva. Imperva Cloud WAF protects hundreds of thousands of websites against cyber threats and blocks billions of security events every day. Counters and insights based on security events are calculated daily and used by users from multiple departments. Millions of counters are added daily, together […]

Customer service organizations today face an immense opportunity. As customer expectations grow, brands have a chance to creatively apply new innovations to transform the customer experience. Although meeting rising customer demands poses challenges, the latest breakthroughs in conversational artificial intelligence (AI) empowers companies to meet these expectations. Customers today expect timely responses to their questions […]

In this post, we show you how to evaluate the performance, trustworthiness, and potential biases of your RAG pipelines and applications on Amazon Bedrock. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

In today’s fast-paced digital world, streamlining workflows and boosting productivity are paramount. That’s why we’re thrilled to share an exciting integration that will take your team’s collaboration to new heights. Get ready to unlock the power of generative artificial intelligence (AI) and bring it directly into your Slack workspace. Imagine the possibilities: Quick and efficient […]

Name entity recognition (NER) is the process of extracting information of interest, called entities, from structured or unstructured text. Manually identifying all mentions of specific types of information in documents is extremely time-consuming and labor-intensive. Some examples include extracting players and positions in an NFL game summary, products mentioned in an AWS keynote transcript, or […]

In this post, we explore a comprehensive solution for addressing the challenges of securing a virtual travel agent powered by generative AI. We provide an end-to-end example and its accompanying code to demonstrate how to implement prompt engineering techniques, content moderation, and various guardrails to make sure the assistant operates within predefined boundaries by relying on Guardrails for Amazon Bedrock. Additionally, we delve into monitoring strategies to track the activation of these safeguards, enabling proactive identification and mitigation of potential issues.

This post explains a generative artificial intelligence (AI) technique to extract insights from business emails and attachments. It examines how AI can optimize financial workflow processes by automatically summarizing documents, extracting data, and categorizing information from email attachments. This enables companies to serve more clients, direct employees to higher-value tasks, speed up processes, lower expenses, enhance data accuracy, and increase efficiency.

In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. However, traditional document processing workflows often involve complex and time-consuming manual tasks, hindering productivity and scalability. In this post, we discuss an approach that uses the […]