Authors - Rashmi Y Matt, Shreya Srinivasan, Venkata Sravani Revuri, Vismaya Murali, Chandravva Hebbi, Natarajan Abstract - Preparing for technical interviews has become very challenging for computer science students due to highly competitive hiring environments and the lack of company-specific practice resources. Existing resources and Generative platforms provide generic questions that do not reflect the specific patterns, technical focus areas, or expectations of different requirements.To address this gap, we present a system that combines a structured knowledge-graph-based retrieval module with a fine-tuned LLamA-2-7B model to generate company-specific technical interview questions. The data set contains 28,854 curated questions from 470 companies, which were cleaned and used for finetuning. The proposed framework also integrates an evaluation pipeline using both LLM-as-a-Judge and manual scoring to check validity, clarity, and technical correctness.The fine-tuned LLamA-2-7B model integrated with the knowledge graph retrieval achieved the best performance, which significantly outperformed other generative models in producing contextually appropriate and technically relevant questions. This approach aims to provide students with more targeted preparation resources aligned with real-world hiring expectations.
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