Gnani.ai Interview Questions
The questions to prepare for Gnani.ai interviews, across all roles. Questions from real interview reports rank first. Updated daily.
Design an LLM serving architecture that manages long generation latency, GPU efficiency, reliability, and model quality.
Design an LLM serving architecture that balances throughput, latency, batching, memory use, and reliability.
Explain how you prioritize competing enterprise accounts using deal risk, value, timing, and stakeholder alignment.
Manage a complex sale by requalifying often, multi-threading stakeholders, and controlling requirement drift with a clear mutual plan.
Explain context windows, tokenization, and the main technical issues with long-context LLM inputs, plus practical ways to handle them.
Explain when to use prompt engineering versus fine-tuning, including quality, cost, latency, data, and maintenance tradeoffs.
Select and justify the evaluation metrics needed to move an LLM from prototype testing to reliable production monitoring.
Explain how embeddings and vector databases fit into a retrieval pipeline for grounded AI responses.
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