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1. Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency

   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.

2. Build public-facing generative AI applications using Amazon Q Business for anonymous users

   Today, we’re excited to announce that Amazon Q Business now supports anonymous user access. With this new feature, you can now create Amazon Q Business applications with anonymous user mode, where user authentication is not required and content is publicly accessible. In this post, we demonstrate how to build a public-facing generative AI application using Amazon Q Business for anonymous users.

3. FloQast builds an AI-powered accounting transformation solution with Anthropic’s Claude 3 on Amazon Bedrock

   In this post, we share how FloQast built an AI-powered accounting transaction solution using Anthropic’s Claude 3 on Amazon Bedrock.

4. Insights in implementing production-ready solutions with generative AI

   As generative AI revolutionizes industries, organizations are eager to harness its potential. However, the journey from production-ready solutions to full-scale implementation can present distinct operational and technical considerations. This post explores key insights and lessons learned from AWS customers in Europe, Middle East, and Africa (EMEA) who have successfully navigated this...

5. Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS

   In this post, we explore how AWS services can be seamlessly integrated with open source tools to help establish a robust red teaming mechanism within your organization. Specifically, we discuss Data Reply’s red teaming solution, a comprehensive blueprint to enhance AI safety and responsible AI practices.

6. InterVision accelerates AI development using AWS LLM League and Amazon SageMaker AI

   This post demonstrates how AWS LLM League’s gamified enablement accelerates partners’ practical AI development capabilities, while showcasing how fine-tuning smaller language models can deliver cost-effective, specialized solutions for specific industry needs.

7. Improve Amazon Nova migration performance with data-aware prompt optimization

   In this post, we present an LLM migration paradigm and architecture, including a continuous process of model evaluation, prompt generation using Amazon Bedrock, and data-aware optimization. The solution evaluates the model performance before migration and iteratively optimizes the Amazon Nova model prompts using user-provided dataset and objective metrics.

8. Customize Amazon Nova models to improve tool usage

   In this post, we demonstrate model customization (fine-tuning) for tool use with Amazon Nova. We first introduce a tool usage use case, and gave details about the dataset. We walk through the details of Amazon Nova specific data formatting and showed how to do tool calling through the Converse and Invoke APIs in Amazon Bedrock. After getting the baseline results from Amazon Nova models, we...

9. Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judge

   In this post, we introduced the Open Source Bedrock Agent Evaluation framework, a Langfuse-integrated solution that streamlines the agent development process. We demonstrated how this evaluation framework can be integrated with pharmaceutical research agents. We used it to evaluate agent performance against biomarker questions and sent traces to Langfuse to view evaluation metrics across...

10. Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

    In this post, the AWS and Cisco teams unveil a new methodical approach that addresses the challenges of enterprise-grade SQL generation. The teams were able to reduce the complexity of the NL2SQL process while delivering higher accuracy and better overall performance.