Source URL: https://www.ilograph.com/blog/posts/diagrams-ai-can-and-cannot-generate/
Source: Hacker News
Title: Diagrams AI can, and cannot, generate
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text discusses the potential and limitations of using generative AI to create system architecture diagrams, focusing on three key use cases: generating generic diagrams, whiteboarding proposed systems, and diagramming real systems from source code. While it highlights some efficiencies in AI-assisted whiteboarding, it ultimately concludes that AI-generated system diagrams from existing code are currently ineffective due to challenges related to training data and code analysis.
Detailed Description:
The article provides a comprehensive exploration of using generative AI for system architecture diagramming, partitioning the discussion into three main use cases:
– **Generic Diagrams**:
– Described as non-specific diagrams, these serve as basic representations of technologies like AWS or Kubernetes.
– The potential quality and utility are limited, as they do not convey any particular solution and rely on plausibility.
– Although generative AI (e.g., ChatGPT) can produce these diagrams, the output is often less visually appealing than a simple image search.
– **Whiteboarding With AI**:
– In this use case, generative AI assists in creating diagrams for future systems with defined functionality, akin to brainstorming sessions.
– The ability to refine and modify AI-generated diagrams demonstrates its utility, but the process is often tedious and can require significant user input.
– The author suggests that for many projects, using diagrams-as-code tools may prove more effective than relying solely on AI due to control and efficiency.
– **System Diagramming from Source Code**:
– This is the most complex use case discussed, emphasizing the need for precise and actionable diagrams that accurately represent deployable systems.
– The text highlights significant shortcomings, noting that AI struggles with producing detailed and accurate diagrams based on existing code due to a lack of training data and the complexity of code.
– The analysis sets forth various challenges, including diverse programming languages present in a codebase and the inherent ambiguity of detailed intent and functionality that may not be well-documented.
**Key Challenges Identified**:
– **Almost No Training Data**: Few publicly available deployable systems with diagrams exist, leading to AI models lacking real-life examples to learn from.
– **Code Analysis Limitations**: AI tools are hampered by the multi-faceted nature of modern codebases, leading to potential inaccuracies in understanding system components.
– **Strategy and Intent**: The need for understanding the purpose behind a system’s architecture is crucial; without this knowledge, generated diagrams may be incoherent or uninformative.
In conclusion, while generative AI has made strides in assisting with preliminary diagramming tasks, it fails to provide the depth and reliability required for complex systems, indicating a significant gap in the tools available to security and engineering professionals. The insights underline that human expertise remains indispensable in understanding and documenting technical systems effectively.