Author: Antonio Rodriguez

In this post, we demonstrate how to implement an automated prompt evaluation system using Amazon Bedrock so you can streamline your prompt development process and improve the overall quality of your AI-generated content.

Today, we’re excited to introduce two powerful new features for Amazon Bedrock: Prompt Management and Prompt Flows, in public preview. These features are designed to accelerate the development, testing, and deployment of generative artificial intelligence (AI) applications, enabling developers and business users to create more efficient and effective solutions that are easier to maintain. You […]

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 Amazon Bedrock Guardrails. Additionally, we delve into monitoring strategies to track the activation of these safeguards, enabling proactive identification and mitigation of potential issues.

The fields of natural language processing (NLP), natural language understanding (NLU), and related branches of machine learning (ML) for text analysis have rapidly evolved to address use cases involving text classification, summarization, translation, and more. State-of-the art, general-purpose architectures such as transformers are making this evolution possible. Looking at text classification in particular, a supervised […]