Next steps and resources
This guide discusses a set of document-level challenges for RAG applications and best practices to mitigate them. These learnings are curated from interviewing and discussing with industry leaders, and they are backed by enterprise use cases.
To begin optimizing your documents for RAG applications, we recommend conducting an audit of your existing documents. Identify areas that pose challenges to the RAG application. Examples include a lack of structure, ambiguous language, or excessive use of graphical elements. Prioritize documents that are frequently accessed or critical to your business operations. Collaborate with subject matter experts to implement the best practices in this guide. Make sure that documents are restructured with clear headings, concise language, and context-setting elements. For new documents, establish guidelines and templates that ensure consistency and help authors adhere to the best practices. Additionally, consider investing in tools or services that can automate aspects of the document optimization process, such as using generative AI to restructure documents. By taking a proactive approach to document optimization, you can unlock the full potential of RAG applications and drive more accurate and insightful results across your organization.
The following resources can help you understand and build RAG applications in your organization.
Resources
AWS documentation
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Choosing an AWS vector database for RAG use cases (AWS Prescriptive Guidance)
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Deploy a RAG use case on AWS by using Terraform and Amazon Bedrock (AWS Prescriptive Guidance)
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Develop advanced generative AI chat-based assistants by using RAG and ReAct prompting (AWS Prescriptive Guidance)
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Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases (Amazon Bedrock documentation)
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Retrieval Augmented Generation (Amazon SageMaker AI documentation)
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Retrieval Augmented Generation options and architectures on AWS (AWS Prescriptive Guidance)
Other AWS resources
