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@article{196903,
        author = {JEENOKANTH G and MOHAMMED UNAIS N and LOHIT U and VIKRAM R},
        title = {AI-POWERED TRAINING DATA CURATION BOT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5089-5096},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196903},
        abstract = {The Training Data Curation Bot is a comprehensive and intelligent AI-driven system designed to automate the process of generating high-quality training datasets from unstructured documents such as PDFs, text files, web pages, and spreadsheets. The primary objective of the system is to simplify and streamline the preparation of training data required for fine-tuning and building domain-specific AI models, eliminating the need for manual data extraction and formatting. The system provides users with an intuitive interface to upload documents through a centralized platform. Once uploaded, the documents are automatically processed using a robust document loading pipeline that detects file types, extracts textual content, and converts it into a standardized internal format. The extracted content is further cleaned, segmented into meaningful text chunks, and prepared for AI driven task execution. Using predefined templates and specialized AI task generators, the system automatically creates structured training examples such as question–answer pairs, summaries, and classification data. Each generated training example undergoes a quality evaluation process to ensure relevance, consistency, and usability. The validated data is then organized into datasets with configurable training, validation, and testing splits, making it directly usable for machine learning and large language model (LLM) fine-tuning. The application supports both command-line and web-based interaction, featuring a modern dashboard that enables real-time monitoring of document processing, training data generation, and quality metrics. The backend is implemented using Python and asynchronous processing techniques to ensure high performance, scalability, and efficient resource management, while the frontend dashboard provides a user-friendly and visually intuitive experience. This automated system significantly reduces human effort, minimizes errors, and improves the speed and reliability of training data preparation. It ensures transparency, traceability, and scalability by maintaining structured data models, detailed logs, and comprehensive quality reports. By transforming raw documents into high-quality AI-ready datasets, the Training Data Curation Bot demonstrates the practical application of artificial intelligence, natural language processing, and full-stack system design.},
        keywords = {},
        month = {April},
        }