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Collaborative project built at Kaggle Week 26 GenAI Hackathon, contributed the core AI engineering layers of a Business Law RAG Assistant that answers legal document queries with source citations and live faithfulness verification.
My contributions: built the hybrid FAISS + BM25 retrieval pipeline for accurate document search; designed the multi-agent orchestration system coordinating retrieval, generation, and verification agents; implemented the faithfulness verification layer to evaluate and score LLM output quality; integrated an MCP tool server enabling live web search as a fallback; and built the analytics dashboard tracking query performance and retrieval metrics.
Built as part of a 4-person team, my work covered the RAG pipeline, agentic architecture, and evaluation infrastructure.
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13 voted
72%
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18 votes
Closed
Keeping the research plan and cited results side by side is a useful way to make the agent’s work easier to inspect. I’d lean toward light mode for reading the table in daylight. How do you distinguish a verified source from one that still needs review?
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