Tag: Generative AI

This post explores Chain-of-Draft (CoD), an innovative prompting technique introduced in a Zoom AI Research paper Chain of Draft: Thinking Faster by Writing Less, that revolutionizes how models approach reasoning tasks. While Chain-of-Thought (CoT) prompting has been the go-to method for enhancing model reasoning, CoD offers a more efficient alternative that mirrors human problem-solving patterns—using concise, high-signal thinking steps rather than verbose explanations.

To address the need for businesses to quickly analyze information and unlock actionable insights, we are announcing Analytics Agent, a new feature that is seamlessly integrated into the GenAI IDP Accelerator. With this feature, users can perform advanced searches and complex analyses using natural language queries without SQL or data analysis expertise. In this post, we discuss how non-technical users can use this tool to analyze and understand the documents they have processed at scale with natural language.

In this post, we explore how Condé Nast used Amazon Bedrock and Anthropic’s Claude to accelerate their contract processing and rights analysis workstreams. The company’s extensive portfolio, spanning multiple brands and geographies, required managing an increasingly complex web of contracts, rights, and licensing agreements.

In this post, we share how Wipro implemented advanced prompt engineering techniques, custom validation logic, and automated code rectification to streamline the development of industrial automation code at scale using Amazon Bedrock. We walk through the architecture along with the key use cases, explain core components and workflows, and share real-world results that show the transformative impact on manufacturing operations.

In this blog post, we show you how agentic workflows can accelerate the processing and interpretation of genomics pipelines at scale with a natural language interface. We demonstrate a comprehensive genomic variant interpreter agent that combines automated data processing with intelligent analysis to address the entire workflow from raw VCF file ingestion to conversational query interfaces.

Graphical user interfaces have carried the torch for decades, but today’s users increasingly expect to talk to their applications. In this post we show how we added a true voice-first experience to a reference application—the Smart Todo App—turning routine task management into a fluid, hands-free conversation.

In this blog post, we explore how TR addressed key business use cases with Open Arena, a highly scalable and flexible no-code AI solution powered by Amazon Bedrock and other AWS services such as Amazon OpenSearch Service, Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, and AWS Lambda. We’ll explain how TR used AWS services to build this solution, including how the architecture was designed, the use cases it solves, and the business profiles that use it.

In this post, we demonstrate how Amazon Nova Premier with Amazon Bedrock can systematically migrate legacy C code to modern Java/Spring applications using an intelligent agentic workflow that breaks down complex conversions into specialized agent roles. The solution reduces migration time and costs while improving code quality through automated validation, security assessment, and iterative refinement processes that handle even large codebases exceeding token limitations.

In this post, we explore how Amazon Bedrock AgentCore Memory transforms raw conversational data into persistent, actionable knowledge through sophisticated extraction, consolidation, and retrieval mechanisms that mirror human cognitive processes. The system tackles the complex challenge of building AI agents that don’t just store conversations but extract meaningful insights, merge related information across time, and maintain coherent memory stores that enable truly context-aware interactions.

In this solution, we demonstrate how the user (a parent) can interact with a Strands or LangGraph agent in conversational style and get information about the immunization history and schedule of their child, inquire about the available slots, and book appointments. With some changes, AI agents can be made event-driven so that they can automatically send reminders, book appointments, and so on.