The Power Of RAG: How Retrieval-Augmented Generation Enhances Generative AI

Generative AI, despite its transformative potential and superior performance in many tasks, is sometimes hindered by unpredictable errors. This has led to caution in fully adopting this technology, especially in high-stakes tasks where even minor errors can have serious consequences. However, technology continues to evolve, and new solutions like Retrieval-Augmented Generation (RAG) are emerging to address these challenges.

RAG is an AI framework that allows a generative AI model to access external information not included in its training data or model parameters. This information, which can be domain-specific or sourced from the internet, augments the model’s internal knowledge base.

RAG works in two steps: retrieval and generation. When a user submits a query, the system searches for relevant information among external documents, adds this to the prompt in the context window, and feeds it into the generative AI model. The model then formulates a response based on its inbuilt knowledge and the additional information from the RAG search.

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