LLM+RAG+Agent: Create an Efficient and Accurate Text Data Labeling System

With the rapid development of generative artificial intelligence, it has shown great potential in many fields, and the construction of text data labeling system has also ushered in new opportunities for change. This paper conducts in-depth research on building an efficient and accurate text data labeling system, focusing on the innovative applications of large language models, RAG, and agents. To overcome the key problem of high-precision intelligent annotation, an intelligent annotation method of vector semantic matching driven by large model was innovatively proposed. Firstly, the problems to be solved by smart labels are analyzed in detail, and then they are reasonably modeled, and then the abstract is extracted with the help of large model driven, and the annotation method design is completed by skillfully matching with the label content system. At the same time, agents play an indispensable auxiliary role in the optimization process of RAG technology search. By deeply analyzing the structure, topic and other characteristics of the input text, the agent can automatically generate accurate search keywords or phrases. After rigorous analysis of the experimental results, the accuracy of the method is verified to be more than 95%, and the application scenarios of the method are described.