Retrieval Enhanced Generation With the rapid development of generative AI, enterprises are increasingly relying on efficient knowledge management to support daily business operations.
In this context, RAG (Retrieval-Augmented Generation) technology has gradually become a key solution. RAG can combine the generation capabilities of large-scale language models (LLM) with real-time data retrieval, greatly improving the efficiency and accuracy of enterprise knowledge bases. This article will delve into the core principles of RAG and its importance as it applies to enterprises.
What is RAG? Innovatie technology
combining generative AI and retrieval
RAG is a technology that combines data retrieval with generative AI . It enhances the response accuracy of generative AI by searching external databases. Traditional LLM (such as the GPT series) mainly relies on the knowledge learned during training when answering questions.
However, as the amount of data
and knowledge that companies need to process continues to grow, the limitations of this approach are becoming increasingly apparent. RAG enables AI to answer based on real-time information by retrieving the latest external data , and is no longer limited to past data.
For businesses, this capability is especially important. Enterprise knowledge bases often need to be updated in real time, and internal information, rules and regulations, or technical specifications may change frequently, and relying solely on LLM to generate answers may result in outdated or incorrect information. RAG can ensure that the generated answers are accurate and meet practical needs by retrieving the rcs database latest information, and is particularly suitable for handling the immediate needs of enterprise knowledge management.
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Application of RAG in enterprises: knowledge tips to make a roof last longer management and knowledge base upgrade
In the field of enterprise knowledge management , the application of RAG is revolutionary. Traditional enterprise knowledge base systems often rely on static data. When employees query questions, they can only search based on existing content. This method is often unsatisfactory in material data efficiency when faced with large and rapidly changing data. RAG can dynamically retrieve external knowledge sources, combine real-time data with internal company data, and provide users with the latest and most accurate answers.