Retrieval-augmented generation (RAG) is the process of improving the output of a large language model (LLM) by combining the strengths of retrieval systems with generative models. It enhances the accuracy and reliability of AI-generated responses by incorporating real-time, contextually relevant information from trusted data repositories.
The success or failure of any AI implementation often hinges on one key factor: data governance. With Gartner predicting that more than half of generative AI deployments in enterprises will fail by 2026, it is vital to understand the significance of data readiness in ensuring the success of your AI initiatives.
Unlock enterprise RAG success with our detailed implementation steps and free templates. Discover how to scope, prioritize, and launch successful AI projects.
Explore how the DoD defines Responsible AI, learn about the RAI Toolkit, and discover how Pryon adheres to each step of the RAI SHIELD Assessment.
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