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About this interview
A technical interview on In-Context Learning & Decoding, 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
Compare decoding strategies: greedy, beam search, top-k, top-p (nucleus), and temperature scaling
Explain how in-context learning enables few-shot generalization without weight updates
Describe self-consistency and inference-time compute scaling in reasoning models
Articulate the tradeoffs between RAG, long-context, and fine-tuning for adding knowledge to LLMs
Topics covered
Decoding StrategiesIn-Context LearningKnowledge IntegrationInference-Time Compute
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through what greedy decoding does at each step when a language model generates text?
“In few-shot prompting, research has shown that the model's performance can be sensitive to which examples you include and even their order. How would you explain why that is?
“Explain what process reward models are and how they differ from outcome reward models in guiding multi-step reasoning at inference time.