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As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. This time efficiency translates to significant cost savings and optimized resource allocation in the review process.
In this post, we propose an end-to-end solution using Amazon Q Business to simplify integration of enterprise knowledgebases at scale. For example, if your dataset includes product descriptions, customer reviews, and technical specifications, you can use relevance tuning to boost the importance of certain fields.
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At AWS re:Invent 2023, we announced the general availability of KnowledgeBases for Amazon Bedrock. With a knowledgebase, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). The correct response is 22,871 thousand square feet.
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They also allow for simpler application layer code because the routing logic, vectorization, and memory is fully managed. It uses the provided conversation history, action groups, and knowledgebases to understand the context and determine the necessary tasks. This text input is captured and sent to the AI assistant.
Its essential for admins to periodically review these metrics to understand how users are engaging with Amazon Q Business and identify potential areas of improvement. The Unsuccessful query responses and Customer feedback metrics help pinpoint gaps in the knowledgebase or areas where the system struggles to provide satisfactory answers.
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Vitech is a global provider of cloud-centered benefit and investment administration software. Retrieval Augmented Generation vs. fine tuning Traditional LLMs don’t have an understanding of Vitech’s processes and flow, making it imperative to augment the power of LLMs with Vitech’s knowledgebase.
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Additionally, the complexity increases due to the presence of synonyms for columns and internal metrics available. The FM generates the SQL query based on the final input. To evaluate the models accuracy and track the mechanism, we store every user input and output in Amazon Simple Storage Service (Amazon S3).
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To maximize accuracy, review the best practices for configuring OpenAPI schema definitions for custom plugins. For an AWS CloudFormation template and code samples to deploy an HR Leave Management System application along with the Amazon Q Business plugin, refer to the following GitHub repo.
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