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RAG – Your system uses HNSW for indexing. In a high-concurrency environment, how do you tune the ‘ef_construction’ and ‘M’ parameters to balance index build time against real-time query throughput ?
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RAG – Interview Q & A
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RAG – Let’s discuss security-aware retrieval. Beyond SID filtering, how did you implement real-time input/output sanitation to prevent “prompt injection” attacks that attempt to bypass the document-level security guardrails you built ?
RAG – Let’s discuss security-aware retrieval. Beyond SID filtering, how did you implement real-time input/output sanitation to prevent “prompt injection” attacks that attempt to bypass the document-level security guardrails you built ? Best Practices : Set 1 Best Practices : Set 2 Real Life Analogy
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RAG – What Is Distibuted Tracing & How To Implement It ?
RAG – Explain Me About Distributed Tracing ?
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RAG – In your asynchronous RAG pipeline, how did you implement distributed tracing to identify whether a latency spike was caused by the embedding model, the vector database, or the LLM generation step ?
RAG – In your asynchronous RAG pipeline, how did you implement distributed tracing to identify whether a latency spike was caused by the embedding model, the vector database, or the LLM generation step ?
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RAG – Let’s talk about MLOps and Evaluation. You utilized Ragas for automated evaluation. Walk me through a diagnostic process for a scenario where your ‘faithfulness’ score was high, but ‘context recall’ was low. What architectural changes would you prioritize ?
RAG – Let’s talk about MLOps and Evaluation. You utilized Ragas for automated evaluation. Walk me through a diagnostic process for a scenario where your ‘faithfulness’ score was high, but ‘context recall’ was low. What architectural changes would you prioritize ?
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RAG – Let’s discuss vector database operations. When scaling your vector clusters to millions of documents, how did you evaluate the trade-off between using Product Quantization (PQ) to save memory versus the potential drop in retrieval precision for niche enterprise jargon ?
RAG – Let’s discuss vector database operations. When scaling your vector clusters to millions of documents, how did you evaluate the trade-off between using Product Quantization (PQ) to save memory versus the potential drop in retrieval precision for niche enterprise jargon ? Answer
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RAG – Let’s talk about your distributed ingestion pipeline. When ingesting petabyte-scale multi-modal data from sources as different as SQL databases and Slack, how did you ensure that the embedding space remained semantically aligned across such disparate data structures ?
RAG – Let’s talk about your distributed ingestion pipeline. When ingesting petabyte-scale multi-modal data from sources as different as SQL databases and Slack, how did you ensure that the embedding space remained semantically aligned across such disparate data structures? Answer
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RAG – How did you architect the vector search to incorporate Security Identifier (SID) filtering without significantly degrading search latency or recall ?
RAG – How did you architect the vector search to incorporate Security Identifier (SID) filtering without significantly degrading search latency or recall ? Answer
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RAG – Describe a situation where you had to choose between a high-performing but expensive model and a smaller, fine-tuned model for a specific RAG task. How did you build the business case for the final decision ?
RAG – Describe a situation where you had to choose between a high-performing but expensive model and a smaller, fine-tuned model for a specific RAG task. How did you build the business case for the final decision?
