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@article{208537,
        author = {Kunal Bendure and Chaitanya Shinde and Ashwini Gagare},
        title = {Generative AI And Large Language Models in Intelligent Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {417-419},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208537},
        abstract = {Over the last couple of years, Generative AI and Large Language Models (LLMs) have quietly become part of how intelligent systems get built. What used to be narrow, rule-bound software is now doing things like writing content, helping with decisions, and holding conversations that feel almost natural. This paper is our attempt to make sense of where things currently stand: how these models are actually being used across healthcare, education, agriculture, and automation, and what is happening underneath the architectures, the training tricks, the way prompts are written. We have also tried not to gloss over the rough edges. Hallucination, bias, privacy, the cost of just running these things, and the ethical questions that come up once they are deployed at scale all of that gets a fair look here too. Toward the end, we talk about what responsible development might actually look like and where there is still real work left to do. Mostly, we just wanted to write something a student or early researcher could pick up and come away with a clear-eyed sense of what Generative AI can do well, and where it still trips up.},
        keywords = {Generative AI; Large Language Models (LLMs); Intelligent Systems; Healthcare; Education; Agriculture; Automation; Prompt Engineering; Responsible AI; Explainable AI; Model Architecture; Ethical AI},
        month = {September},
        }