Category: HAQM Bedrock

In this post, we highlight the advanced data augmentation techniques and performance improvements in HAQM 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.

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

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.

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

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

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 question types.

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.

The AFX team’s product migration to the Nova Lite model has delivered tangible enterprise value by enhancing sales workflows. By migrating to the HAQM 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.

In this post, we walk you through how to build a hybrid search solution using OpenSearch Service powered by multimodal embeddings from the HAQM Titan Multimodal Embeddings G1 model through HAQM Bedrock. This solution demonstrates how you can enable users to submit both text and images as queries to retrieve relevant results from a sample retail image dataset.

In this post, we explore two approaches for securing sensitive data in RAG applications using HAQM Bedrock. The first approach focused on identifying and redacting sensitive data before ingestion into an HAQM Bedrock knowledge base, and the second demonstrated a fine-grained RBAC pattern for managing access to sensitive information during retrieval. These solutions represent just two possible approaches among many for securing sensitive data in generative AI applications.