Category: Technical How-to

We demonstrate two methods for generating structured responses with Amazon Bedrock: Prompt Engineering and Tool Use with the Converse API. Prompt Engineering is flexible, works with Bedrock models (including those without Tool Use support), and handles various schema types (e.g., Open API schemas), making it a great starting point. Tool Use offers greater reliability, consistent results, seamless API integration, and runtime validation of JSON schema for enhanced control.

In this post, we demonstrate how to use SageMaker AI to apply the Random Cut Forest (RCF) algorithm to detect anomalies in spacecraft position, velocity, and quaternion orientation data from NASA and Blue Origin’s demonstration of lunar Deorbit, Descent, and Landing Sensors (BODDL-TP).

In this post, we demonstrate how to build a multi-agent system using multi-agent collaboration in Amazon Bedrock Agents to solve complex business questions in the biopharmaceutical industry. We show how specialized agents in research and development (R&D), legal, and finance domains can work together to provide comprehensive business insights by analyzing data from multiple sources.

Amazon SageMaker Canvas offers no-code solutions that simplify data wrangling, making time series forecasting accessible to all users regardless of their technical background. In this post, we explore how SageMaker Canvas and SageMaker Data Wrangler provide no-code data preparation techniques that empower users of all backgrounds to prepare data and build time series forecasting models in a single interface with confidence.

In this post, we present a benchmark of different understanding models from the Amazon Nova family available on Amazon Bedrock, to provide insights on how you can choose the best model for a meeting summarization task.

Developing robust text-to-SQL capabilities is a critical challenge in the field of natural language processing (NLP) and database management. The complexity of NLP and database management increases in this field, particularly while dealing with complex queries and database structures. In this post, we introduce a straightforward but powerful solution with accompanying code to text-to-SQL using a custom agent implementation along with Amazon Bedrock and Converse API.

In this post, we explore how generative AI can revolutionize threat modeling practices by automating vulnerability identification, generating comprehensive attack scenarios, and providing contextual mitigation strategies.

This post provides instructions to configure a structured data retrieval solution, with practical code examples and templates. It covers implementation samples and additional considerations, empowering you to quickly build and scale your conversational data interfaces.

In this post, we show you how to build generative AI applications on Amazon Web Services (AWS) using the capabilities of Amazon Bedrock, highlighting how Amazon Bedrock can be used at each step of your generative AI journey. This guide is valuable for both experienced AI engineers and newcomers to the generative AI space, helping you use Amazon Bedrock to its fullest potential.

In this post, we demonstrate how we combined Amazon Nova, Amazon Rekognition, and Amazon Polly to automate the creation of accessible audio descriptions for video content. This approach can significantly reduce the time and cost required to make videos accessible for visually disabled audiences.