Category: HAQM Aurora

RAG retrieves data from a preexisting knowledge base (your data), combines it with the LLM’s knowledge, and generates responses with more human-like language. However, in order for generative AI to understand your data, some amount of data preparation is required, which involves a big learning curve. In this post, we walk you through how to convert your existing Aurora data into an index without needing data preparation for HAQM Kendra to perform data search and implement RAG that combines your data along with LLM knowledge to produce accurate responses.

In this post, we walk you through configuring and integrating HAQM Q for Business with Aurora PostgreSQL-Compatible to enable your database administrators, data analysts, application developers, leadership, and other teams to quickly get accurate answers to their questions related to the content stored in Aurora PostgreSQL databases.

In this post, we dive deep into the vector database options available as part of HAQM Bedrock Knowledge Bases and the applicable use cases, and look at working code examples.

In this post we describe how AWS generative AI solutions (including HAQM Bedrock, HAQM Q Developer, and HAQM Q Business) can further enhance TechOps productivity, reduce time to resolve issues, enhance customer experience, standardize operating procedures, and augment knowledge bases.

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In recent years, businesses are increasingly looking for ways to integrate the power of machine learning (ML) into business decision-making. This post demonstrates the use case of creating an optimal incentive program to offer customers identified as being at risk of leaving for a competitor, or churning. It extends a popular ML use case, predicting […]

In recent years, AWS customers have been running machine learning (ML) on an increasing variety of datasets and data sources. Because a large percentage of organizational data is stored in relational databases such as HAQM Aurora, there’s a common need to make this relational data available for training ML models, and to use ML models […]

In this blog post, we will show you how to get started building rich text analytics views from your database, without having to learn anything about machine learning for natural language processing models. We’ll do this by leveraging HAQM Comprehend, paired with HAQM Aurora-MySQL and AWS Lambda.