Algorithmic Generation of GraphQL Schema Definitions Using Large Language Models Trained on Open-Source APIs

Authors

Baric Finck Skala
AI Platform Engineer / LLM Integration Engineer , Australia

Keywords:

GraphQL, Large Language Models, Schema Generation, Open-Source APIs, Transformer Models, Automation, AI-driven Development

Synopsis

This paper explores the use of Large Language Models (LLMs) for the algorithmic generation of GraphQL schema definitions derived from open-source API documentation. By leveraging pretrained transformer architectures fine-tuned on structured API datasets, schema components such as queries, mutations, and type relationships can be automatically constructed with minimal developer input. The study evaluates model accuracy, schema validity, and integration efficiency with Node.js and Apollo Server environments. Experiments reveal that LLM-generated schemas maintain functional correctness while reducing development effort by over 60%. Furthermore, schema optimization techniques improve model-driven inference consistency across heterogeneous API sources, underscoring the potential of AI-augmented schema engineering.

 

 

 

 

References

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Published

June 22, 2022

Series

Research paper

Categories

Copyright (c) 2022 Baric Finck Skala (Author)

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.