In the blink of an eye, 2024 is nearing its end. This year, the “Huawei Expert Live Room” has successfully held 7 live broadcasts, sharing Huawei’s experience in industry digital transformation, covering construction, steel, non-ferrous metals, smelting, transportation, oil and gas, and continuously shaping the brand image of “digital transformation partners”. Recently, some friends commented that they often encounter knowledge management issues at work: knowledge managementis chaotic and time-consuming to search, and there are serious information silos, making knowledge difficult to share, leading to repeated work among team members; they hope to arrange a live broadcast to introduce how to solve knowledge management issues through intelligent means.
Shortly, I will contact Huawei AI expert Teacher Shi Qiquan to consult him. Recently, Teacher Shi has been deeply researching how the large model + knowledge base (RAG) can be rapidly implemented in enterprises, and it is most appropriate to invite him to address the issues of knowledge management intelligence.
🎓 Teacher Shi Qiquan: This can be resolved by building a knowledge base. In the era of information explosion, various industries face challenges and opportunities brought by massive data, and effectively transforming data into knowledge is crucial for enterprise development. The industry knowledge base serves as the core platform connecting data and wisdom, helping enterprises improve efficiency and reduce decision-making errors. With its powerful semantic understanding and generation capabilities, large models can automatically construct industry knowledge bases, enhancing efficiency, reducing costs, and achieving dynamic updates, specifically referring to the technology combination of large models + knowledge bases (RAG).
🎓 Teacher Shi Qiquan: The RAG (Retrieval-Augmented Generation) system platform, formed by combining large models with knowledge bases and retrieval technology, effectively addresses the high cost and hallucination issues of large model implementation. RAG constrains the generation process of large models with precise professional knowledge from the industry knowledge base, providing enterprises with fast, accurate, and comprehensive knowledge services, meeting the demand for low-cost application of large models and promoting the digital transformation of enterprises.
🎓 Teacher Shi Qiquan: In the era of large models, enterprise knowledge bases are no longer just simple data piles; they require intelligent and efficient knowledge management and utilization. The application scenarios of large model + knowledge base (RAG) include intelligent search, intelligent review, intelligent customer service, and unified knowledge management, which can efficiently transform massive data into knowledge, build a global intelligent knowledge management system, and facilitate interaction and integration between people and knowledge, as well as between business and knowledge. Through the large model + knowledge base (RAG) system platform, the process shifts from people searching for knowledge and businesses accumulating knowledge to knowledge actively and accurately finding people, allowing knowledge content to deeply influence specific businesses, truly helping enterprises improve quality and efficiency.
So, how exactly should we build an industry knowledge base based on large models? Are there any successful cases of applying large model + knowledge base (RAG) in the industry? What technical explorations does Huawei have in the large model + knowledge base (RAG) area? To unlock the answers to these questions, please pay attention to Teacher Shi’s upcoming live broadcast – “Large Model + Knowledge Base (RAG): How to Enable Industry Digitalization?”.
This live broadcast will use the large model knowledge base as a starting point to clearly explain the value of the knowledge base, detail the process of combining large models with knowledge bases, and share practical experiences in using large model + knowledge base (RAG) to enable industry digital transformation. The live broadcast will start at ⏰ 19:00 on November 27 (Wednesday). Interested friends can scan the QR code below 👇 or click “Read the original text” at the end of the article to make an appointment for the live broadcast!
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