Category: Generative AI

In this post, we demonstrate how to use the Open AI Whisper foundation model (FM) Whisper Large V3 Turbo, available in HAQM Bedrock Marketplace, which offers access to over 140 models through a dedicated offering, to produce near real-time transcription. These transcriptions are then processed by HAQM Bedrock for summarization and redaction of sensitive information.

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 HAQM Bedrock Knowledge Bases.

In this post, we’ll explore how to combine HAQM 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.

We’ve witnessed remarkable advances in model capabilities as generative AI companies have invested in developing their offerings. Language models such as Anthropic’s Claude Opus 4 & Sonnet 4 and HAQM Nova on HAQM Bedrock can reason, write, and generate responses with increasing sophistication. But even as these models grow more powerful, they can only work […]

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 HAQM Bedrock to identify relationships between regulation changes and SOPs.

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 HAQM.com examples of such generative AI applications.

In this post, we give an overview of a well-established generative AI foundation, dive into its components, and present an end-to-end perspective. We look at different operating models and explore how such a foundation can operate within those boundaries. Lastly, we present a maturity model that helps enterprises assess their evolution path.

HAQM 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.

In this post, we explore how a leading GeoFM (Clay Foundation’s Clay foundation model available on Hugging Face) can be deployed for large-scale inference and fine-tuning on HAQM SageMaker.

In this post, we focus on the HAQM Nova Canvas image generation model. We then provide an overview of the image generation process (diffusion) and dive deep into the input parameters for text-to-image generation with HAQM Nova Canvas.