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International Advanced Research Journal in Science, Engineering and Technology
International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal
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← Back to VOLUME 12, ISSUE 6, JUNE 2025

Code Genie: AI- Driven Code Generation with Optimization and Commenting

Harshita Deogade, Dhanraj Jadhav, Prajakta Ugale, Anuj Vibhute, Prof. N. G. Bhojne

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Abstract: This paper introduces a cutting-edge AI-powered web application designed to revolutionize interview preparation. Built using Next.js, integrated with large language model (LLM) APIs, and enhanced by modern UI frameworks, the platform delivers an interactive and personalized experience. Key features include AI-generated mock interviews, structured roadmaps for technical learning, book-based study content, and dynamic performance feedback. Unlike traditional LLM interactions such as ChatGPT, this application provides goal-oriented, context-aware guidance with an intuitive UI/UX. It bridges learning gaps through a structured approach and leverages analytics to offer actionable insights, enabling users to track progress and improve systematically throughout their preparation journey.

Keywords: AI Interview Preparation, Next.js, Full-stack Development, Gemini API, LLM, TailwindCSS, Roadmaps, Automated Feedback, EdTech, React.js.

How to Cite:

[1] Harshita Deogade, Dhanraj Jadhav, Prajakta Ugale, Anuj Vibhute, Prof. N. G. Bhojne, “Code Genie: AI- Driven Code Generation with Optimization and Commenting,” International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2025.12625

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.