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

Flipboard

For enterprise data, a major difficulty stems from the common case of database tables having embedded structures that require specific knowledge or highly nuanced processing (for example, an embedded XML formatted string). This optional step has the most value when there are many named resources and the lookup process is complex.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

Prerequisites Before proceeding with this tutorial, make sure you have the following in place: AWS account – You should have an AWS account with access to Amazon Bedrock. When you send a message to a model, you can provide definitions for one or more tools that could potentially help the model generate a response.

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Datasaur: The Definitive Guide to LLM-Automated Labeling

ODSC - Open Data Science

In the evolving field of natural language processing (NLP), data labeling remains a critical step in training machine learning models. To get started with LLM-automated labeling, select a foundational model from OpenAI, AWS Bedrock, Microsoft Azure, HuggingFace, or other providers available in Datasaurs LLM Labs.

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Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights

AWS Machine Learning Blog

Prerequisites Before proceeding, make sure that you have the necessary AWS account permissions and services enabled, along with access to a ServiceNow environment with the required privileges for configuration. AWS Have an AWS account with administrative access. For AWS Secrets Manager secret, choose Create and add a new secret.

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How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations

AWS Machine Learning Blog

The AML feature store standardizes variable definitions using scientifically validated algorithms. Discover and its transactional and batch applications are deployed and scaled on a Kubernetes on AWS cluster to optimize performance, user experience, and portability. The following diagram illustrates the solution architecture.

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Automate building guardrails for Amazon Bedrock using test-driven development

AWS Machine Learning Blog

Prerequisites Before you start, make sure you have the following prerequisites in place: Create an AWS account , or sign in to your existing account. Make sure that you have the correct AWS Identity and Access Management (IAM) permissions to use Amazon Bedrock. Have access to the large language model (LLM) that will be used.

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How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results

AWS Machine Learning Blog

Measures Assistant is a microservice deployed in a Kubernetes on AWS environment and accessed through a REST API. The Measures Assistant prompt template contains the following information: A general definition of the task the LLM is running. The following diagram illustrates the solution architecture.