Walk me through how you built your RAG Enterprise AI Assistant — from retrieval to generation to evaluation — and how you ensured production readiness

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Anonymous · Accepted Answer

I used FAISS as the vector database with a hybrid search strategy — combining dense embeddings from GPT-4 with keyword-based retrieval to improve recall. I implemented intelligent chunking with overlap and preserved document metadata so the model knew exactly which source each chunk came from. This was critical for traceability and compliance

Anonymous · Answer

I started by indexing over 50,000 enterprise documents — support tickets, product documentation, and internal knowledge bases. I chose FAISS as my vector database because it's highly performant at scale and integrates well with LangChain.

Anonymous · Answer

I started with FAISS as my vector database because it's highly performant at scale. I implemented a hybrid retrieval strategy — combining dense embeddings from OpenAI's text-embedding-3 model with sparse keyword-based retrieval using BM25. This ensured I captured both semantic meaning and exact term matches

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