Category: Technical How-to

In today’s data-intensive business landscape, organizations face the challenge of extracting valuable insights from diverse data sources scattered across their infrastructure. Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query and use this wealth of information. In this post, we explore how you can use Amazon […]

LLM training has seen remarkable advances in recent years, with organizations pushing the boundaries of what’s possible in terms of model size, performance, and efficiency. In this post, we explore how FP8 optimization can significantly speed up large model training on Amazon SageMaker P5 instances.

In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails.

Amazon Bedrock Guardrails helps implement safeguards for generative AI applications based on specific use cases and responsible AI policies. Amazon Bedrock Guardrails assists in controlling the interaction between users and foundation models (FMs) by detecting and filtering out undesirable and potentially harmful content, while maintaining safety and privacy. In this post, we explore a solution that automates building guardrails using a test-driven development approach.

In this post, we show you how DXC and AWS collaborated to build an AI assistant using large language models (LLMs), enabling users to access and analyze different data types from a variety of data sources. The AI assistant is powered by an intelligent agent that routes user questions to specialized tools that are optimized for different data types such as text, tables, and domain-specific formats. It uses the LLM’s ability to understand natural language, write code, and reason about conversational context.

This blog post discusses a solution that combines AWS Resilience Hub and Amazon Bedrock to generate architectural findings in natural language. By using the capabilities of Resilience Hub and Amazon Bedrock, you can share findings with C-suite executives, engineers, managers, and other personas within your corporation to provide better visibility over maintaining a resilient architecture.

In this post, we demonstrate how to interact with the Amazon Titan Image Generator G1 v2 model on Amazon Bedrock to generate an image. Then, we show you how to use Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock to describe it, evaluate it with a score from 1–10, explain the reason behind the given score, and suggest improvements to the image.

In this post, we introduce the core dimensions of responsible AI and explore considerations and strategies on how to address these dimensions for Amazon Bedrock applications.

This post focuses on doing RAG on heterogeneous data formats. We first introduce routers, and how they can help managing diverse data sources. We then give tips on how to handle tabular data and will conclude with multimodal RAG, focusing specifically on solutions that handle both text and image data.

In this post, we walk through a step-by-step guide to automating invoice processing using Streamlit and Amazon Bedrock, addressing the challenge of handling invoices from multiple vendors with different formats. We show how to set up the environment, process invoices stored in Amazon S3, and deploy a user-friendly Streamlit application to review and interact with the processed data.