Category: Amazon Machine Learning

Contextual retrieval enhances traditional RAG by adding chunk-specific explanatory context to each chunk before generating embeddings. This approach enriches the vector representation with relevant contextual information, enabling more accurate retrieval of semantically related content when responding to user queries. In this post, we demonstrate how to use contextual retrieval with Anthropic and Amazon Bedrock Knowledge Bases.

In this post, we’ll explore how to combine Amazon Bedrock prompt caching with Claude Code—a coding agent released by Anthropic that is now generally available. This powerful combination transforms your development workflow by delivering lightning-fast responses from reducing inference response latency, as well as lowering input token costs.

The Danish humanitarian organization Bevar Ukraine has developed a comprehensive virtual generative AI-powered assistant called Victor, aimed at addressing the pressing needs of Ukrainian refugees integrating into Danish society. This post details our technical implementation using AWS services to create a scalable, multilingual AI assistant system that provides automated assistance while maintaining data security and GDPR compliance.

In this post, we explore how Noodoe uses AI and Amazon Bedrock to optimize EV charging operations. By integrating LLMs, Noodoe enhances station diagnostics, enables dynamic pricing, and delivers multilingual support. These innovations reduce downtime, maximize efficiency, and improve sustainability. Read on to discover how AI is transforming EV charging management.

In this post, we explore how to use Graph-based Retrieval-Augmented Generation (GraphRAG) in Amazon Bedrock Knowledge Bases to build intelligent applications. Unlike traditional vector search, which retrieves documents based on similarity scores, knowledge graphs encode relationships between entities, allowing large language models (LLMs) to retrieve information with context-aware reasoning.

When a regulatory body like the US Food and Drug Administration (FDA) introduces changes to regulations, organizations are required to evaluate the changes against their internal SOPs. When necessary, they must update their SOPs to align with the regulation changes and maintain compliance. In this post, we show different approaches using Amazon Bedrock to identify relationships between regulation changes and SOPs.

ZURU collaborated with AWS Generative AI Innovation Center and AWS Professional Services to implement a more accurate text-to-floor plan generator using generative AI. In this post, we show you why a solution using a large language model (LLM) was chosen. We explore how model selection, prompt engineering, and fine-tuning can be used to improve results.

Non-conversational applications offer unique advantages such as higher latency tolerance, batch processing, and caching, but their autonomous nature requires stronger guardrails and exhaustive quality assurance compared to conversational applications, which benefit from real-time user feedback and supervision. This post examines four diverse Amazon.com examples of such generative AI applications.

OpenSearch offers a wide range of third-party machine learning (ML) connectors to support this augmentation. This post highlights two of these third-party ML connectors. The first connector we demonstrate is the Amazon Comprehend connector. In this post, we show you how to use this connector to invoke the LangDetect API to detect the languages of ingested documents. The second connector we demonstrate is the Amazon Bedrock connector to invoke the Amazon Titan Text Embeddings v2 model so that you can create embeddings from ingested documents and perform semantic search.

Amazon Bedrock Model Copy and Model Share features provide a powerful option for managing the lifecycle of an AI application from development to production. In this comprehensive blog post, we’ll dive deep into the Model Share and Model Copy features, exploring their functionalities, benefits, and practical applications in a typical development-to-production scenario.