Where AI excels and fails at generating system architecture diagrams

Generative AI has shown remarkable capabilities in creating text and images, but its ability to generate accurate system architecture diagrams varies significantly by use case. Understanding these limitations is crucial for developers and architects looking to leverage AI for diagramming tasks, as the technology currently offers value primarily in ideation stages rather than in documenting complex existing systems.

The big picture: AI’s capability to generate system architecture diagrams varies dramatically across three distinct use cases, with effectiveness decreasing as the need for accuracy and specificity increases.

• Generic technology-focused diagrams and whiteboarding scenarios represent AI’s current sweet spot for diagram generation.
• Generating diagrams of existing, complex systems remains largely beyond AI’s current capabilities due to fundamental limitations in training data and code analysis.

Key details: Generic diagrams explaining technologies like AWS or Kubernetes work reasonably well with AI since they don’t require the same level of accuracy as real system documentation.

• These diagrams are often decorative or educational rather than functional, giving AI significant leeway in their creation.
• The visual explanation of general technology concepts aligns with the pattern-matching capabilities of current AI systems.

Whiteboarding use case: AI shows promise in diagramming proposed future systems during planning and exploration phases.

• This middle-ground use case requires more detail than generic diagrams but less precision than documenting existing systems.
• AI can help explore potential solutions and identify problems during early-stage system design.

The limitations: Generating diagrams for real-life existing systems faces at least three significant technical barriers.

• There’s virtually no training data available, as detailed system diagrams rarely appear online where AI models could learn from them.
• Code analysis remains challenging for AI, particularly when dealing with verbose and complex system architectures.
• Source code alone doesn’t convey the strategic purpose and business context essential for comprehensive system documentation.

Why this matters: Accurate system diagrams serve critical functions in software development, from onboarding new team members to guiding incident response.

• The inability of AI to generate reliable diagrams of existing systems means human expertise remains essential for this important documentation.
• The contrast between AI’s capabilities in creative ideation versus documentation highlights the technology’s current strengths and limitations.

Reading between the lines: The challenges in AI-generated system diagramming reflect broader limitations in AI’s understanding of complex, context-dependent systems.

• The article suggests that the value of manually creating diagrams extends beyond documentation to learning and understanding systems.
• This represents a clear boundary of current AI capabilities where human investigation and learning remain irreplaceable.