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Gen AI News Talk

Llama 4 family of models from Meta are now available in SageMaker JumpStart

Today, we’re excited to announce the availability of Llama 4 Scout and Maverick models in Amazon SageMaker JumpStart and coming soon in Amazon Bedrock. Llama 4 represents Meta’s most advanced multimodal models to date, featuring a mixture of experts (MoE) architecture and context window support...

Effectively use prompt caching on Amazon Bedrock

Prompt caching, now generally available on Amazon Bedrock with Anthropic’s Claude 3.5 Haiku and Claude 3.7 Sonnet, along with Nova Micro, Nova Lite, and Nova Pro models, lowers response latency by up to 85% and reduces costs up to 90% by caching frequently used...

Multi-tenancy in RAG applications in a single Amazon Bedrock knowledge base with metadata filtering

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies and AWS. Amazon Bedrock Knowledge Bases offers fully managed, end-to-end Retrieval Augmented Generation (RAG) workflows to create highly accurate, low-latency, secure, and custom generative...

Advanced tracing and evaluation of generative AI agents using LangChain and Amazon SageMaker AI MLFlow

Developing generative AI agents that can tackle real-world tasks is complex, and building production-grade agentic applications requires integrating agents with additional tools such as user interfaces, evaluation frameworks, and continuous improvement mechanisms. Developers often find themselves grappling with unpredictable behaviors, intricate workflows, and a...

Prompting for the best price-performance

In the drive to remain competitive, businesses today are turning to AI to help them minimize cost and maximize efficiency. It’s incumbent on them to find the most suitable AI model—the one that will help them achieve more while spending less. For many businesses,...

Evaluate models or RAG systems using Amazon Bedrock Evaluations – Now generally available

Organizations deploying generative AI applications need robust ways to evaluate their performance and reliability. When we launched LLM-as-a-judge (LLMaJ) and Retrieval Augmented Generation (RAG) evaluation capabilities in public preview at AWS re:Invent 2024, customers used them to assess their foundation models (FMs) and generative...