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@article{192484,
        author = {Mrs.Bhavya and Dr.K.Santhosh kumar},
        title = {Prompt Engineering Revealed: An Extensive Analysis of Novel Approaches and Significant Results},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1185-1189},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192484},
        abstract = {Prompt engineering refers to the systematic design and optimization of input queries or instructions provided to Natural Language Processing (NLP) models, with the goal of influencing their output. This abstract explores the significance of prompt engineering in enhancing the performance and interpretability of NLP models. By carefully crafting prompts, practitioners can guide models to produce desired responses, improve robustness, and mitigate biases. The abstract delves into various techniques and methodologies employed in prompt engineering, highlighting its impact on model behavior across different applications. Additionally, it discusses challenges, ethical considerations, and future directions in the evolving field of prompt engineering within the broader context of responsible AI development.},
        keywords = {Text Generation, Natural Language Processing (NLP), Prompt Design, Performance Optimization, Human AI Interaction.},
        month = {February},
        }