The large model can automatically generate requirements documents and codes for low-code developers through natural language understanding. It also has automatic capabilities such as automatic detection and repair of code errors, automatic optimization of code, identification of redundancy, and efficient solutions, bringing developers Changes in demand mode, design mode, and development mode save time and cost, improve code quality, and further reduce the developer's threshold and learning costs.
More importantly, through the self-learning ability of large models for documents, templates, business processes, samples, and source codes, and the integration of low-code design layout and logic optimization capabilities, the low-code positioning form is upgraded, the development boundary is expanded, and the value range is opened. , it is foreseeable that a low-code development platform that integrates large-scale model capabilities is expected to become an accelerator for the implementation of GPT 2B applications. Therefore, we need to redefine the low-code development platform.
Wang Enyao said that the integration of low-code and large-scale models brings both opportunities and challenges. The opportunity lies in that it can truly empower non-professional developers, realize that all roles can participate in the development process, and through continuous training and accumulation, strengthen the deep integration of large models and low-code, and play the value of intelligent low-code development.
The challenge is that , on the one hand, driven by the emerging market demands, the combination of low-code acceleration and private domain data is forced to complete application development and deployment more efficiently and accurately, and at the same time meet the guarantee standards of high availability, high reliability, and high security . On the other hand, large models need to be integrated with domain knowledge to reduce application costs by engineering large models, and at the same time ensure the security and reliability of private domain data to avoid business risks brought by large models.