What is Retrieval Augmented Generation

What is Retrieval Augmented Generation

Introduction

Retrieval-augmented generation (RAG) is a cutting-edge technique that combines the power of retrieval-based and generative models to enhance natural language processing tasks. One of the most prominent examples of this technology is the large language model (LLM) developed by Open AI.

Large Language Models (LLMs) have transformed the AI landscape, enabling unparalleled capabilities in natural language understanding and generation. However, their limitations include occasional inaccuracy and lack of source information. This can be problematic in contexts where accuracy and traceability are essential. RAG overcomes this limitation.

The figure below shows a typical data flow for RAG solutions

For RAG systems, the question from end users goes through a retrieval system first and then into the enterprise data source to fetch all relevant data related to the question. This retrieved information is used as context for LLM, and question + context, as well as instructions, are sent to the LLM model to get precise and accurate information.

This way RAG leverages the strength of both retrieval-based and generative models to improve the accuracy and fluency of natural language processing tasks by incorporating the relevant information from a large text database, RAG can generate more coherent and contextually appropriate responses. 

What is the need for RAG?

A lot of time enterprise information is saved in various applications and systems that act as silos of information. Business owners and executives have to hop through several reports and dashboards to retrieve accurate information. Sometimes these reports get outdated before they are reviewed

For example, an executive for a company that sells a product as well as supports customers can question LLM for an accurate picture of how many products are sold as well as how many support issues are resolved, and which product has the most issues. LLM can answer all these questions if the appropriate RAG system is set up

Are there any security risks if a large language model has access to all information in the enterprise

Yes, there is always a risk when you iterate and aggregate information from diverse data sources. RAG systems reduce this via

  1. The retrieval system is integrated with an enterprise single sign-on system (e.g., OKTA), so if a user does not have access and permission to specific data stores, the retrieval system will not retrieve and index that information, so LLM will not have information that is not authorized for a particular user. 
  2. For legacy systems that do not use OAuth and OpenID authentications, RAG retrievers also fetch authorization for users and groups who can view this information. this helps RAG retrieval process to stop unauthorized access to information
  3. RAG pipelines have strong governance rules that can further limit unauthorized access to information even if the retriever has retrieved it from the source system. For example, PII rules can prevent anyone from getting an employee address and other protected information

Are RAG systems costly?

Most large language models run on expansive GPU machines so setting up a private and dedicated infrastructure can be costly. However, AWS and Goole provide great and cost-effective generative infrastructure like Google Vertax.AI and AWS Bedrock that can be used to build very cost-effective RAG solutions.  Open source, as well as private models, can be used to generate responses. The High Plains team can help you build RAG solutions that can fit your budget

Are there any pre-build RAG services that can be easily configured and used

Yes Amazon has recently launched AWS Q, The High Plains team can quickly set up and configure information retrieval data sources and create an RAG application for you in days and weeks

Conclusion

Retrieval Augmented Generation represents a significant advancement in natural language processing technology, offering improved accuracy, fluency, and context in language tasks. By combining the strengths of retrieval-based and generative models, RAG has the potential to enhance a wide range of applications in the field of artificial intelligence. If your organization requires any assistance with Retrieval Augmented Generation (RAG), please don’t hesitate to contact HighPlains Computing

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