GraphQL Data Mocking at Scale with LLMs and @generateMock
Published:Oct 30, 2025 17:01
•1 min read
•Airbnb Engineering
Analysis
This article from Airbnb Engineering likely discusses their approach to generating mock data for GraphQL APIs using Large Language Models (LLMs) and a custom directive, potentially named `@generateMock`. The focus would be on how they've scaled this process, implying challenges in generating realistic and diverse mock data at a large scale. The use of LLMs suggests leveraging their ability to understand data structures and generate human-like responses, which is crucial for creating useful mock data for testing and development. The `@generateMock` directive likely provides a convenient way to integrate this functionality into their GraphQL schema.
Key Takeaways
- •Airbnb leverages LLMs to generate realistic mock data for GraphQL APIs.
- •The `@generateMock` directive simplifies the integration of mock data generation into the GraphQL schema.
- •The approach addresses the challenges of scaling data mocking for large-scale applications.
Reference
“The article likely highlights the benefits of using LLMs for data mocking, such as improved realism and reduced manual effort.”