Retrieval-augmented generation (RAG) is a prominent architecture used to enhance AI assistants by merging language models with external knowledge sources. This system addresses the limitations of large language models, such as providing accurate and cited responses, thus reducing misinformation. However, implementing a production-ready RAG architecture involves significant engineering challenges, necessitating a thorough understanding of embedding models, similarity metrics, and retrieval techniques. The article discusses essential components for building these systems and shares lessons learned from deploying RAG-based solutions in real-world applications, tailored for engineers working on AI assistant projects.
Retrieval augmented generation (RAG) has quickly risen to become one of the most popular architectures when building AI assistants, especially in scenarios where combining the power of language models with proprietary information is key.
Integrating an external knowledge base with transformer models using a RAG architecture allows generative AI systems not only to provide more accurate and factual responses, but also to cite its sources when responding.
#ai #retrieval-augmented-generation #embedding-models #machine-learning #natural-language-processing
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