Alexander College Interview Questions
The questions to prepare for Alexander College interviews, across all roles. Questions from real interview reports rank first. Updated daily.
Design a high-throughput RAG pipeline that grounds answers, detects unsupported claims, and balances quality, latency, and cost.
Explain context windows, tokenization, and the main technical issues with long-context LLM inputs, plus practical ways to handle them.
Compare fine-tuning with high-quality context across quality, cost, latency, maintainability, and safety.
Implement seeded random-hyperplane locality-sensitive hashing to return the most similar high-dimensional vectors.
Design a practical framework for comparing LLM quality, reliability, latency, cost, and safety in production.
Design an LLM serving architecture that manages long generation latency, GPU efficiency, reliability, and model quality.
Design a scalable embedding pipeline and approximate nearest-neighbor index for semantic text retrieval.
Design an evaluation framework for measuring both the quality and diversity of generative model outputs.
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