Retrieval-augmented generation (RAG) combines data retrieval with generative AI models to improve the accuracy and contextual relevance of responses in enterprise applications. Traditional systems, including relational databases and search tools, faced limitations in processing unstructured data and adapting to changing taxonomies. RAG enables natural language understanding and generation, allowing large language models (LLMs) to interpret queries, retrieve relevant documents, and produce human-like responses. This innovative approach addresses issues of outdated information and inaccuracies often found in traditional AI models, making it a crucial development for future AI applications.
Retrieval-augmented generation (RAG) transforms AI systems by blending data retrieval with generative language models, facilitating accurate and contextually relevant responses to user queries.
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