Retrieval-Augmented Generation (RAG) can improve the accuracy of conversational AI applications by connecting language models with reliable, domain-specific information. Instead of generating responses only from pre-trained knowledge, RAG retrieves relevant data from documents, databases, knowledge bases, or APIs before producing an answer.
This helps conversational systems provide more relevant and up-to-date responses while reducing unsupported outputs. By combining embeddings, vector databases, semantic search, reranking, and contextual retrieval, Conversational AI Development can deliver intelligent interactions that better understand user intent and provide reliable, context-aware responses.
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