Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Hallucination & Reliability, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain the primary causes of LLM hallucination: training data gaps, knowledge cutoffs, and overgeneralization
Describe SelfCheckGPT and consistency-based hallucination detection approaches
Explain mitigation strategies: RAG grounding, chain-of-verification, and confidence calibration
Describe the difference between intrinsic hallucination and extrinsic hallucination in the RAG context
Topics covered
Hallucination fundamentalsRoot causesHallucination taxonomyDetection challengesDetection methodsMitigation: RAGMitigation: promptingConfidence calibrationInference-time controlsMitigation strategiesRAG-specific evaluationRAG-specific failure modesTraining-time mitigationsProduction reliability
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“How would you define hallucination in the context of large language models, and why is it particularly dangerous compared to other model errors?
“How would you use log probabilities from an LLM to flag potentially hallucinated tokens or spans? What are the limitations of this approach?
“How do RLHF and preference alignment affect hallucination rates? Can alignment training make hallucinations worse in certain ways?