AWS Machine Learning Blog
Category: Artificial Intelligence
Using transcription confidence scores to improve slot filling in Amazon Lex
When building voice-enabled chatbots with Amazon Lex, one of the biggest challenges is accurately capturing user speech input for slot values. Transcription confidence scores can help ensure reliable slot filling. This blog post outlines strategies like progressive confirmation, adaptive re-prompting, and branching logic to create more robust slot filling experiences.
Improving Retrieval Augmented Generation accuracy with GraphRAG
Lettria, an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.
Add a generative AI experience to your website or web application with Amazon Q embedded
Amazon Q embedded is a feature that lets you embed a hosted Amazon Q Business assistant on your website or application to create more personalized experiences that boost end-users’ productivity. In this post, we demonstrate how to use the Amazon Q embedded feature to add an Amazon Q Business assistant to your website or web application using basic HTML or React.
An introduction to preparing your own dataset for LLM training
In this blog post, we provide an introduction to preparing your own dataset for LLM training. Whether your goal is to fine-tune a pre-trained model for a specific task or to continue pre-training for domain-specific applications, having a well-curated dataset is crucial for achieving optimal performance.
Design multi-agent orchestration with reasoning using Amazon Bedrock and open source frameworks
This post provides step-by-step instructions for creating a collaborative multi-agent framework with reasoning capabilities to decouple business applications from FMs. It demonstrates how to combine Amazon Bedrock Agents with open source multi-agent frameworks, enabling collaborations and reasoning among agents to dynamically execute various tasks. The exercise will guide you through the process of building a reasoning orchestration system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents, and FMs. We also explore the integration of Amazon Bedrock Agents with open source orchestration frameworks LangGraph and CrewAI for dispatching and reasoning.
How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model
Fastweb, one of Italy’s leading telecommunications operators, recognized the immense potential of AI technologies early on and began investing in this area in 2019. In this post, we explore how Fastweb used cutting-edge AI and ML services to embark on their LLM journey, overcoming challenges and unlocking new opportunities along the way.
Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights
In this post, we’ll demonstrate how to configure an Amazon Q Business application and add a custom plugin that gives users the ability to use a natural language interface provided by Amazon Q Business to query real-time data and take actions in ServiceNow.
Simplify multimodal generative AI with Amazon Bedrock Data Automation
Amazon Bedrock Data Automation in public preview, offers a unified experience for developers of all skillsets to easily automate the extraction, transformation, and generation of relevant insights from documents, images, audio, and videos to build generative AI–powered applications. In this post, we demonstrate how to use Amazon Bedrock Data Automation in the AWS Management Console and the AWS SDK for Python (Boto3) for media analysis and intelligent document processing (IDP) workflows.
How TUI uses Amazon Bedrock to scale content creation and enhance hotel descriptions in under 10 seconds
TUI Group is one of the world’s leading global tourism services, providing 21 million customers with an unmatched holiday experience in 180 regions. The TUI content teams are tasked with producing high-quality content for its websites, including product details, hotel information, and travel guides, often using descriptions written by hotel and third-party partners. In this post, we discuss how we used Amazon SageMaker and Amazon Bedrock to build a content generator that rewrites marketing content following specific brand and style guidelines.
Llama 3.3 70B now available in Amazon SageMaker JumpStart
Today, we are excited to announce that the Llama 3.3 70B from Meta is available in Amazon SageMaker JumpStart. Llama 3.3 70B marks an exciting advancement in large language model (LLM) development, offering comparable performance to larger Llama versions with fewer computational resources. In this post, we explore how to deploy this model efficiently on Amazon SageMaker AI, using advanced SageMaker AI features for optimal performance and cost management.