Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is an AI framework that improves large language models by connecting them to external information sources, such as real-time data, proprietary knowledge bases, or document repositories, during prompt execution. By retrieving relevant facts and combining them with the model's generative abilities, RAG produces more accurate, up-to-date, and context-aware responses.
Why it Matters:
It reduces hallucinations and ensures outputs are accurate, verifiable, and contextually relevant.
QAT Global uses RAG to ground generative AI models in client-owned data, ensuring reliability for enterprise applications. Our IT Staffing services focus on recruiting AI developers with experience in retrieval pipelines, vector databases, and prompt engineering for client projects.
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