Improving OpenAI and Gemini AI API Responses via Schema Validation for Python Developers

The rapid adoption of AI application programming interfaces (APIs), such as those from OpenAI and Google’s Gemini, has exposed challenges in achieving consistent, structured outputs for software development, often requiring complex post-processing to align responses with desired data formats. This paper presents easyaipy , an open-source Python library designed to enhance the reliability and efficiency of AI API interactions by enforcing developer-specified schemas and supporting multimodal inputs. The library’s modular architecture integrates prompt modification, schema validation, and retry mechanisms to ensure machine-readable JSON outputs, streamlining tasks like text-image processing and structured data extraction. Development followed an iterative methodology, with testing across 40 diverse real-world scenarios demonstrating a 100% success rate with OpenAI and near-perfect reliability with Gemini. A larger-scale evaluation with 500 varied requests further confirmed its robustness, yielding 99% schema-compliant responses with OpenAI and 90% with Gemini. A comparative evaluation against standard OpenAI API usage in a slide-generation task revealed easyaipy’s superiority: it achieved a 100% schema adherence rate over 300 trials (versus 80% for the standard approach) while reducing code complexity by 64%, from 69 to 25 lines. These results underscore easyaipy’s ability to simplify AI-driven workflows, making it a valuable tool for researchers and developers.