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Updated weekly · Last refresh Aug 30

Basis Research Institute AI Engineer Interview Questions

The questions to prepare for a Basis Research Institute AI Engineer interview. Questions from real interview reports rank first. Updated weekly.

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1
Generative AI & LLMsStart here. 5 questions · ~40 min
Explain Vector Search in RAGMedium

Design a grounded document Q&A system and explain how vector search improves retrieval quality, latency, and hallucination control in RAG.

Vector SearchPrompt EngineeringRAGBasis Research Institute
Reduce Hallucinations in LLM AnswersEasy

Explain LLM hallucination and give three practical ways to reduce it using grounding, prompting, and evaluation.

HallucinationPrompt EngineeringRAGBasis Research Institute
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2
Behavioral & Leadership4 questions · ~32 min
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3
More topics7 questions · ~56 min
Design an LLM Serving PlatformHard

Design an LLM serving system that balances latency, cost, scalability, and safety for production traffic.

Cold StartFeature StoreModel ServingBasis Research Institute
Diagnose Underperforming ModelMedium

Diagnose why a model is underperforming and decide whether the issue is thresholding, class balance, or a deeper data problem.

Hyperparameter TuningCross-ValidationBias-Variance TradeoffBasis Research Institute
Reducing Overfitting in ML ModelsMedium

Explain how to diagnose and reduce overfitting using regularization, cross-validation, and model selection.

Cross-ValidationBias-Variance TradeoffRegularizationBasis Research Institute
Design Real-Time Operations Dashboard PipelineHard

Design a pipeline for a real-time operational dashboard, covering streaming ingestion, modeling, data quality, and dashboard serving.

InfrastructureStream ProcessingQualityBasis Research Institute
Supervised vs Unsupervised LearningEasy
Recently asked

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance TradeoffBasis Research Institute
Design a Personalized Recommendation RankerHard

Design a personalized recommendation system that turns user preferences into ranked suggestions with retrieval, ranking, and feedback loops.

RetrievalTwo-Tower ModelsRecommendation SystemsBasis Research Institute
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