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Generative AI is widely predicted to transform almost every industry and use case, and companies spent more than $20 billion on the technology last year. But it also exposes these firms to new risks if not implemented strategically. In this blog, we will explain how the Retrieval Augmented Generation (RAG) technique enhances generative AI helps to mitigate these risks and deliver more accurate, relevant and trustworthy results.
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Retrieval Augmented Generation (RAG) is a technique to enhance the results of a generative AI or Large Language Model (LLM) solution. Perhaps the best way to understand RAG is to first look at how generative AI traditionally works, and why that poses a risk to companies seeking to leverage the technology.
A typical generative AI tool which hasn’t been enhanced by Retrieval Augmented Generation will generate a response to a prompt based on its training data and continuous learning from prompts and responses to and from users of the tool. This brings four main risks, which limit the confidence a user can have in its use of generative AI’s outputs:
A Retrieval Augmented Generation technique is regarded as the . This approach forces the generative AI tool to retrieve every response from authoritative and original sources, which supersedes its continuous learning from training data and subsequent prompts and responses. This contextual data will shape the response that is provided to the user based off exact source content in the dataset and can provide a citation within the response.
This brings two significant benefits to companies using generative AI solutions:
MORE: The AI Checklist: 10 best practices to ensure that generative AI meets your needs
The contextual data used in a RAG approach must be credible. This means sourcing data from trustworthy and licensed data providers and publishers. There have been instances of data allegedly being scraped and used in generative AI tools without permission from the publisher or individuals who the data belongs to, which brings legal and reputational risks. Companies must therefore ensure their data has been sourced ethically and be transparent about that.
A large company might have developed their own generative AI solution. In this case, they should think about how to bring in excellent data to support their RAG approach. Alternatively, companies may find it more cost-effective to use third-party generative AI tools to support their operations. These firms should seek to understand how that tool uses and collects data and verify that the provider is trustworthy and compliant.
The C-Suite is responsible for setting and enforcing an ethical AI strategy. Making clear that you only want to use the most reliable and credible data and ensuring your generative AI tool is using a Retrieval Augmented Generation approach which clearly cites sources used to generate each answer, will inspire confidence in your company. 97% of professionals surveyed for the LexisNexis® Future of Work Report 2024 said it is important that human members of staff validate AI outputs, so staff should be trained and empowered to oversee this technology and look out for potential inaccuracies or regulatory breaches.
MORE: How to Develop an Ethical AI Approach
Using a Retrieval Augmented Generation technique for generative AI is only effective if the contextual data it brings in is accurate, trustworthy, and approved for use in generative AI tools. LexisNexis provides licensed content and optimized technology to support your generative AI and RAG ambitions:
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