Generative AI for Diagrams as Code and Code as Diagrams

Modern software projects often involve extensive codebases that are difficult to navigate, document, and keep consistent across multiple teams and repositories. Traditional documentation often becomes outdated, leading to inconsistencies and unclear architectural insights. To address this, Generative AI and Large Language Models (LLMs) such as ChatGPT and Claude automate the bidirectional transformation between code and diagrams, seamlessly integrating into software workflows. The proposed framework uses PlantUML and TikZ, to improve debugging, documentation, and versioning. Neo4j is used for natural language retrieval, and advanced queries using Cypher as well as similarity searches. This hybrid approach mitigates LLMrelated challenges like hallucinations by combining database integration with prompt engineering. By consolidating code, diagrams, and metadata into a unified resource, the framework aims to improve maintainability, collaboration, and transparency, as demonstrated in initial qualitative use cases.