Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Prompt Engineering Fundamentals, pitched at the easy 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 four elements of a well-structured prompt: instruction, context, input data, and output indicator
Contrast zero-shot, one-shot, and few-shot prompting and explain when each is appropriate
Describe LLM inference parameters (temperature, top-p, max tokens) and their effect on output
Explain chain-of-thought prompting and why it improves reasoning on multi-step tasks
Topics covered
Prompt engineering definitionPrompt elementsZero-shot promptingFew-shot promptingZero-shot vs few-shotFew-shot limitationsOne-shot promptingLLM inference parametersChain-of-thought promptingPrompt elements appliedPrompt design best practicesPrompt structure for chat modelsIn-context learningAdvanced prompting techniques
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In your own words, what is prompt engineering and why does it matter when working with large language models?
“What does the max tokens parameter control, and what are the practical consequences of setting it too low versus too high?
“What is the concept of in-context learning, and how does it relate to few-shot prompting at a mechanistic level?