Category: Foundation models

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.

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 Amazon Nova on Amazon 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 Amazon Bedrock to identify relationships between regulation changes and SOPs.

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 Amazon SageMaker.

In this post, we show you how to implement and evaluate three powerful techniques for tailoring FMs to your business needs: RAG, fine-tuning, and a hybrid approach combining both methods. We provid ready-to-use code to help you experiment with these approaches and make informed decisions based on your specific use case and dataset.

Amazon Bedrock Data Automation helps organizations streamline development and boost efficiency through customizable, multimodal analytics. It eliminates the heavy lifting of unstructured content processing at scale, whether for video or audio. The new capabilities make it faster to extract tailored, generative AI-powered insights like scene summaries, key topics, and customer intents from video and audio. This unlocks the value of unstructured content for use cases such as improving sales productivity and enhancing customer experience.

In this post, we share comprehensive best practices and scientific insights for fine-tuning Meta Llama 3.2 multimodal models on Amazon Bedrock. By following these guidelines, you can fine-tune smaller, more cost-effective models to achieve performance that rivals or even surpasses much larger models—potentially reducing both inference costs and latency, while maintaining high accuracy for your specific use case.

In this post, we highlight the advanced data augmentation techniques and performance improvements in Amazon Bedrock Model Distillation with Meta’s Llama model family. This technique transfers knowledge from larger, more capable foundation models (FMs) that act as teachers to smaller, more efficient models (students), creating specialized models that excel at specific tasks.

The AFX team’s product migration to the Nova Lite model has delivered tangible enterprise value by enhancing sales workflows. By migrating to the Amazon Nova Lite model, the team has not only achieved significant cost savings and reduced latency, but has also empowered sellers with a leading intelligent and reliable solution.