Free Research Note!
Mastering the Open-Book Test – Why and How Retrieval-Augmented Generation Improves GenAI Outcomes
Gen AI has a trust problem. From hallucinations to opaque processing of sensitive data – language models such as GPT-4, LLAMA-3 and many more do not seem to be enterprise-ready without further governance. Read this research note to find out why Retrieval Augmented Generation can be a governed approach to AI in your organization that is also performant and robust.
Chief data officers and other data leaders should read this report to learn:
- Why RAG is a versatile approach for the application of generative AI in companies and what strengths it has aroundacorrect, complete answers, but also data governance
- Why RAG can differentiate your company in a competitive market, harnessing Gen AI to increase customer satisfaction or for more operational efficiency
- Which technologies are used to implement RAG and how they interact
- What fundamental principles we can derive from the experiences of early adopters for the success of your RAG project
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Further information
Retrieval-augmented generation, or RAG, selects and feeds domain-specific data into language models to improve their accuracy. We define RAG, its challenges, benefits, must-have characteristics and common use cases. We then evaluate three architectural approaches – vector RAG for semantics, relational RAG for database values, and graph RAG for ontologies – and provide an example of a hybrid solution that combines all three approaches. Data and AI leaders that read this report will learn the value of RAG and guiding principles to get started.
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