Category: Advanced (300)

In this post, we demonstrate a solution using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Developer Experience, and Amazon Personalize that allow marketers to save time and deliver efficient personalized advertising using a generative AI enhanced solution. Our solution is a marketing agent that shows how Amazon Personalize can effectively segment target customers based on relevant characteristics and behaviors. Additionally, by using Amazon Bedrock Agents and foundation models (FMs), our tool generates personalized creative content specifically tailored to each purpose. It customizes the tone, creative style, and individual preferences according to each customer’s specific prompt, providing highly customized and effective marketing communications.

In this post, we demonstrate how to fine-tune Meta’s latest Llama 3.2 text generation models, Llama 3.2 1B and 3B, using Amazon SageMaker JumpStart for domain-specific applications. By using the pre-built solutions available in SageMaker JumpStart and the customizable Meta Llama 3.2 models, you can unlock the models’ enhanced reasoning, code generation, and instruction-following capabilities to tailor them for your unique use cases.

While organizations continue to discover the powerful applications of generative AI, adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. In the first part of the series, we showed how AI administrators can build a […]

The rise of large language models (LLMs) and foundation models (FMs) has revolutionized the field of natural language processing (NLP) and artificial intelligence (AI). These powerful models, trained on vast amounts of data, can generate human-like text, answer questions, and even engage in creative writing tasks. However, training and deploying such models from scratch is […]

Druva enables cyber, data, and operational resilience for thousands of enterprises, and is trusted by 60 of the Fortune 500. In this post, we show how Druva approached natural language querying (NLQ)—asking questions in English and getting tabular data as answers—using Amazon Bedrock, the challenges they faced, sample prompts, and key learnings.

AWS offers powerful generative AI services, including Amazon Bedrock, which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. Many businesses want to integrate these cutting-edge AI capabilities with their existing collaboration tools, such as Google Chat, to […]

A number of organizations use Gmail for their business email needs. Gmail for business is part of Google Workspace, which provides a set of productivity and collaboration tools like Google Drive, Gmail, and Google Calendar. Google Drive supports storing documents such as Emails contain a wealth of information found in different places, such as within […]

This post demonstrates how to build an IDP pipeline for automatically extracting and processing data from documents using Amazon Bedrock Prompt Flows, a fully managed service that enables you to build generative AI workflow using Amazon Bedrock and other services in an intuitive visual builder. Amazon Bedrock Prompt Flows allows you to quickly update your pipelines as your business changes, scaling your document processing workflows to help meet evolving demands.

This post is part of an ongoing series on governing the machine learning (ML) lifecycle at scale. To start from the beginning, refer to Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker. A multi-account strategy is essential not only for improving governance but also for enhancing […]

In this post, we cover the core concepts behind RAG architectures and discuss strategies for evaluating RAG performance, both quantitatively through metrics and qualitatively by analyzing individual outputs. We outline several practical tips for improving text retrieval, including using hybrid search techniques, enhancing context through data preprocessing, and rewriting queries for better relevance.