Category: Amazon Bedrock Knowledge Bases

In this post, we demonstrate how Octus migrated its flagship product, CreditAI, to Amazon Bedrock, transforming how investment professionals access and analyze credit intelligence. We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including Amazon Bedrock, AWS Fargate, and Amazon OpenSearch Service.

Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.

In this post, we’ll explore how to leverage Amazon Bedrock, LlamaIndex, and RAGAS to enhance your RAG implementations. You’ll learn practical techniques to evaluate and optimize your AI systems, enabling more accurate, context-aware responses that align with your organization’s specific needs.

Amazon Bedrock Knowledge Bases has a metadata filtering capability that allows you to refine search results based on specific attributes of the documents, improving retrieval accuracy and the relevance of responses. These metadata filters can be used in combination with the typical semantic (or hybrid) similarity search. In this post, we discuss using metadata filters with Amazon Bedrock Knowledge Bases.

In this post, we demonstrate how to implement this evaluation framework using Amazon Bedrock, compare the performance of different generator models, including Anthropic’s Claude and Amazon Nova on Amazon Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.

This post introduces a solution to reduce hallucinations in Large Language Models (LLMs) by implementing a verified semantic cache using Amazon Bedrock Knowledge Bases, which checks if user questions match curated and verified responses before generating new answers. The solution combines the flexibility of LLMs with reliable, verified answers to improve response accuracy, reduce latency, and lower costs while preventing potential misinformation in critical domains such as healthcare, finance, and legal services.

In this post, we explain how F1 and AWS have developed a root cause analysis (RCA) assistant powered by Amazon Bedrock to reduce manual intervention and accelerate the resolution of recurrent operational issues during races from weeks to minutes. The RCA assistant enables the F1 team to spend more time on innovation and improving its services, ultimately delivering an exceptional experience for fans and partners. The successful collaboration between F1 and AWS showcases the transformative potential of generative AI in empowering teams to accomplish more in less time.

In this post, we explore the movement of RAG applications from their proof of concept or minimal viable product (MVP) phase to full-fledged production systems. When transitioning a RAG application from a proof of concept to a production-ready system, optimization becomes crucial to make sure the solution is reliable, cost-effective, and high-performing.

Untold Studios is a tech-driven, leading creative studio specializing in high-end visual effects and animation. This post details how we used Amazon Bedrock to create an AI assistant (Untold Assistant), providing artists with a straightforward way to access our internal resources through a natural language interface integrated directly into their existing Slack workflow.

Aetion is a leading provider of decision-grade real-world evidence software to biopharma, payors, and regulatory agencies. In this post, we review how Aetion is using Amazon Bedrock to help streamline the analytical process toward producing decision-grade real-world evidence and enable users without data science expertise to interact with complex real-world datasets.