Category: Generative AI

In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process. We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. This solution automates portions of the WAFR report creation, helping solutions architects improve the efficiency and thoroughness of architectural assessments while supporting their decision-making process.

In this two-part series, we discuss how you can reduce the DeepSeek model customization complexity by using the pre-built fine-tuning workflows (also called “recipes”) for both DeepSeek-R1 model and its distilled variations, released as part of Amazon SageMaker HyperPod recipes. In this first post, we will build a solution architecture for fine-tuning DeepSeek-R1 distilled models and demonstrate the approach by providing a step-by-step example on customizing the DeepSeek-R1 Distill Qwen 7b model using recipes, achieving an average of 25% on all the Rouge scores, with a maximum of 49% on Rouge 2 score with both SageMaker HyperPod and SageMaker training jobs. The second part of the series will focus on fine-tuning the DeepSeek-R1 671b model itself.

This guide demonstrates how to deploy an open source foundation model from Hugging Face on Amazon EC2 instances across three locations: a commercial AWS Region and two AWS Local Zones. Through comparative benchmarking tests, we illustrate how deploying foundation models in Local Zones closer to end users can significantly reduce latency—a critical factor for real-time applications such as conversational AI assistants.

In this post, we walk through how to discover, deploy, and use the Mistral AI Pixtral 12B model for a variety of real-world vision use cases.

This post explains how to integrate Smartsheet with Amazon Q Business to use natural language and generative AI capabilities for enhanced insights. Smartsheet, the AI-enhanced enterprise-grade work management platform, helps users manage projects, programs, and processes at scale.

In this post, we show how Anthropic’s Claude 3.5 Sonnet in Amazon Bedrock can be used for a variety of business-related cognitive tasks, such as problem-solving, critical thinking and ideation—to help augment human thinking and improve decision-making among knowledge workers to accelerate innovation.

In this post, we demonstrate how to implement this evaluation framework using Amazon Bedrock, compare the performance of different generator models, including Anthropic’s Claude and Amazon Nova on Amazon Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.

This post demonstrates how Amazon Bedrock Agents, combined with action groups and generative AI models, streamlines and accelerates the resolution of Terraform errors while maintaining compliance with environment security and operational guidelines.

In this post, we present the continuous self-instruct fine-tuning framework as a compound AI system implemented by the DSPy framework. The framework first generates a synthetic dataset from the domain knowledge base and documents for self-instruction, then drives model fine-tuning through SFT, and introduces the human-in-the-loop workflow to collect human and AI feedback to the model response, which is used to further improve the model performance by aligning human preference through reinforcement learning (RLHF/RLAIF).

In this post, we show you how to connect Amazon Q, a generative AI-powered assistant, to Amazon FSx for Windows File Server to securely analyze, query, and extract insights from your file system data.