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詳細
検索拡張世代 (RAG)、CPU、モデル最適化手法の組み合わせにより、レイテンシー、忠実度、スケーラビリティーという推論エンジン品質の 3 つの要素が得られます。
使用方法に関する説明
関連アセット
タイトルと説明
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アクション
Transform Retail with Retrieval Augmented Generation (RAG): The Future of Personalized Shopping
This solution uses advanced retrieval techniques for current recommendations that are tailored to rapidly changing customer preferences.
LLM Retrieval-Augmented Generation (RAG) with OpenVINO and LangChain
Companies that want to deploy an AI application (such as a support chatbot) can use OpenVINO and LangChain to implement an efficient RAG pipeline, and utilize OpenVINO's benefits.
Driving Enterprise RAG Innovation with Intel® Xeon® Processors
FoundationFlow, Bud Ecosystem, and Intel collaborate for an innovative RAG solution, delivering 60% improvement in handling product catalog queries.
Optimize Retrieval-Augmented Generation Performance and TCO — Solution Brief
This solution brief outlines a reference design for an Intel-optimized Retrieval-Augmented Generation (RAG) solution and demonstrates its compatibility with industry-standard software components.
Case Study: AI Sweden Adopts Intel® Xeon® Processors and Intel® Gaudi® Accelerators for Prototype Virtual Assistant
RAG architecture with annotated training data can help public sector employees collaborate and access relevant information faster.