RAG-powered Q&A system combines dense retrieval from domain-specific knowledge bases with LLM rea...RAG-powered Q&A system combines dense retrieval from domain-specific knowledge bases with LLM rea...
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Shreya Singh
• Feb 18
 RAG-powered Q&A system combines dense retrieval from domain-specific knowledge bases with LLM reasoning, achieving 92% accuracy on technical queries while reducing hallucinations by 85% and enabling 3x faster enterprise search. Built with Pinecone vector DB + Llama 3.1, it grounds responses in verified sources for trustworthy AI decision-making
AI Engineer Data ScienceMachine Learning EngineeringLangChainLangFlowPython
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