Retrieval-Augmented Generation

Charles Liu

2025 · 3 citations

Retrieval-augmented generation (RAG) is a hybrid architecture that combines the generative power of large language models (LLMs) with the factual reliability of information retrieval systems. Although the emergence of large language models (LLMs) has significantly improved the performance of natural language understanding and generation tasks. However, these models often suffer from information distortion, outdated information, and lack of transparency. Retrieval-augmented generation (RAG) addresses these limitations by introducing an external retrieval mechanism into the generation process.…

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