Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Prompt Engineering Fundamentals, 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 self-consistency prompting and how majority-vote sampling improves accuracy
Describe Tree-of-Thoughts (ToT) and how it differs from linear chain-of-thought
Explain ReAct prompting and how it interleaves reasoning traces with tool-use actions
Compare prompt chaining, program-aided language models (PAL), and meta-prompting
Topics covered
Self-consistencyTree-of-ThoughtsReAct promptingPrompt chainingProgram-aided language modelsMeta-promptingReflexionTechnique comparisonApplied technique selectionCost and tradeoffsActive-PromptSelf-evaluation and metacognition
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Chain-of-thought prompting improves multi-step reasoning. What problem does self-consistency address that standard chain-of-thought still has?
“When you design a prompt chain, how do you decide where to draw the boundary between one prompt and the next?
“Imagine you are building a system to answer complex research questions. You have to choose among ReAct, ToT, and prompt chaining. How do you reason through the tradeoffs?