RAG (Retrieval-Augmented Generation)
RAG combines a large language model (LLM) with an external knowledge retrieval component to ground AI responses in factual, up-to-date data.
Why it Matters:
It reduces hallucinations and enhances the reliability of generative AI by connecting models to trusted information sources.
QAT Global can implement RAG architectures in enterprise applications to ensure accuracy and data lineage for clients. IT staffing for these types of projects includes recruiting AI engineers experienced in vector databases, embeddings, and prompt chaining for production-grade RAG systems.
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