Automating Domain-Driven Design: Experience with a Prompting Framework
Summary: arXiv:2603.26244v1 Announce Type: cross
Domain-driven design (DDD) is a powerful design technique for architecting complex software systems. This article discusses a newly introduced prompting framework that automates core DDD activities through structured interactions with large language models (LLMs). By breaking down DDD into five sequential steps, this framework aims to enhance the design process while allowing for greater efficiency and collaboration among stakeholders.
Core Steps of the Prompting Framework
The prompting framework decomposes DDD into the following five sequential steps:
- Establishing an Ubiquitous Language: This step focuses on creating a common language that all team members can understand, ensuring effective communication throughout the project.
- Simulating Event Storming: Here, the framework facilitates event storming sessions that help teams identify the key events in the system, allowing for a better grasp of the domain.
- Identifying Bounded Contexts: This step involves defining the boundaries of different subdomains, which aids in managing complexity and enhancing collaboration.
- Designing Aggregates: In this step, the framework assists teams in structuring the aggregates that encapsulate the domain logic.
- Mapping to Technical Architecture: Finally, the framework helps translate the design into technical architecture, aligning software components with domain requirements.
Case Study with FTAPI’s Enterprise Platform
In a practical application, the prompting framework was validated against real-world requirements from FTAPI’s enterprise platform. The evaluation revealed that while the initial steps consistently produced valuable and actionable artifacts, challenges arose in the later stages. Specifically, minor errors or inaccuracies in the earlier steps had a tendency to propagate, leading to impractical outputs during the aggregation and technical architecture mapping.
Findings and Implications
The findings from this study highlight the dual role of LLMs in the DDD process. While the framework serves as an excellent collaborative sparring partner for producing documentation, such as glossaries and context maps, it does not fully automate the design process. This limitation underscores the importance of human expertise in making critical decisions regarding trade-offs within the design.
Conclusion
Overall, the prompting framework demonstrates significant potential for enhancing the efficiency of the DDD process. Steps 1 to 3 showed strong performance, while the complexities introduced in Steps 4 and 5 revealed the need for careful oversight by architectural experts. The research suggests that LLMs can be valuable tools for reducing the effort and overhead associated with DDD, but they should complement rather than replace human-centric decision-making.
As organizations continue to explore innovative methods for software design, the integration of AI-driven frameworks like this one could pave the way for more streamlined and collaborative development processes.