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Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

AWS Machine Learning - AI

Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle.

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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

Were excited to announce the open source release of AWS MCP Servers for code assistants a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. This post is the first in a series covering AWS MCP Servers.

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Camelot Secure’s AI wizard eases path to cybersecurity compliance

CIO

Like many innovative companies, Camelot looked to artificial intelligence for a solution. The result is Myrddin, an AI-based cyber wizard that provides answers and guidance to IT teams undergoing CMMC assessments. However, integrating Myrddin into the CMMC dashboard was just the beginning.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

AWS Machine Learning - AI

The effectiveness of RAG heavily depends on the quality of context provided to the large language model (LLM), which is typically retrieved from vector stores based on user queries. The relevance of this context directly impacts the model’s ability to generate accurate and contextually appropriate responses.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

The introduction of Amazon Nova models represent a significant advancement in the field of AI, offering new opportunities for large language model (LLM) optimization. In this post, we demonstrate how to effectively perform model customization and RAG with Amazon Nova models as a baseline.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

Building cloud infrastructure based on proven best practices promotes security, reliability and cost efficiency. To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. This systematic approach leads to more reliable and standardized evaluations.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

AWS Machine Learning - AI

In this post, we propose an end-to-end solution using Amazon Q Business to simplify integration of enterprise knowledge bases at scale. This solution ingests and processes data from hundreds of thousands of support tickets, escalation notices, public AWS documentation, re:Post articles, and AWS blog posts.