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RAG – Model Quantization
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RAG – Context Pruning
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RAG – Prompt Templates
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RAG – Reranking
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RAG – When moving the RAG system to production workloads, what specific bottlenecks did you encounter in the vector indexing or retrieval phase, and how did you optimize the latency for real-time conversational use ?
When moving the RAG system to production workloads, what specific bottlenecks did you encounter in the vector indexing or retrieval phase, and how did you optimize the latency for real-time conversational use ? Indexing Bottle Neck Retrival Bottle Neck Generation Bottle Neck
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RAG – Mean Reciprocal Gain (MRR)
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RAG – Normalized Discounted Cumulative Gain (NDCG)
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RAG -How did you determine the optimal weighting between vector and keyword search for different document types ?
How did you determine the optimal weighting between vector and keyword search for different document types ?
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RAG – Hybrid Search
RAG – Hybrid Search
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Enterprise RAG Pipeline – Question Set-1
Enterprise RAG Pipeline Insteview Questions Question-1: Let’s talk about your RAG platform. You mentioned implementing hybrid search and reranking. How did you determine the optimal weighting between vector and keyword search for different document types? Question-2: When moving the RAG system to production workloads, what specific bottlenecks did you encounter in the vector indexing or retrieval phase, and how did you optimize the latency for real-time conversational use? Question-3: Your ingestion pipeline handles SharePoint and APIs. How did you design the system to ensure that document-level access controls (ACLs) from the source systems were strictly respected during the retrieval phase?
