LLM-based Scientific Question Answering
Published in Red Hat, 2026
- Built a knowledge-grounded question-answering system using Wikipedia corpus and transformer-based language models.
- Implemented a RAG pipeline combining document retrieval, re-ranking, and LLM answer generation.
- Applied hybrid retrieval and contextual re-ranking techniques to improve answer accuracy and reduce hallucinations.
- Designed preprocessing pipelines for document chunking, indexing, and metadata management.
- Evaluated system performance using curated datasets and automated evaluation metrics for scientific knowledge queries.