Hence, with the rapid increase in generative AI, quite a huge amount of unstructured data in the forms of text, logs, and answer formats are being generated. Capturing this data in the structured manner is required to support efficient storage, retrieval, and analysis. This study aims to create a system that processes unstructured AI-generated data into structured JSON format and stores it in a PostgreSQL database with Java and Spring Boot. The approach is through natural language processing (NLP) techniques, rule-based parsing, and annotation-based transformation to get the relevant information while ensuring data integrity.For validation, the system was implemented in an interview preparation software, where AI-generated answers, feedback, and evaluation metrics were converted into structured JSON and stored for seamless querying and analysis. Performance evaluation showed high parsing accuracy (92%), optimized database query execution (65% faster with JSONB indexing), and low latency API response times (under 200ms per request).The study demonstrates that Java and Spring Boot provide an efficient solution for structuring AI-generated data and integrating it into relational databases, improving data accessibility, scalability, and performance. Future enhancements will focus on real-time data processing, AI-driven parsing optimization, and cloud-based scalability to further refine the system’s capabilities.
Unstructured Data, Generative AI, JSON Parsing, PostgreSQL, Java, Spring Boot, Interview, Data Structuring.
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