Interview questions focused specifically on prompt design, evaluation, and reliability.
What makes a prompt 'good'?
Clarity about the task, sufficient context, an explicit format for the output, and constraints that rule out common failure modes. A good prompt reduces ambiguity to the point where the model has one clear way to succeed.
What is few-shot prompting and when would you use it?
Including a few examples of the desired input-output pattern directly in the prompt. It's especially useful when the output format needs to be precise and consistent, and description alone isn't reliable enough.
How do you debug a prompt that's producing inconsistent results?
Isolate variables — test with temperature at zero to remove randomness as a factor, simplify the prompt to find which part is causing ambiguity, and check whether the issue is actually the prompt or a limitation of the model/context itself.
What's the risk of overly complex prompts?
Long, dense prompts increase the chance the model misses or misweights part of the instruction. Simpler, well-structured prompts — sometimes broken into multiple steps — are often more reliable than one giant instruction.
How would you design a prompt for structured data extraction?
Specify the exact output format (like JSON with named fields), give an example, constrain what happens with missing data explicitly, and consider using the model's structured-output or function-calling features if available rather than relying on formatting instructions alone.
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Our Prompt Engineering program builds the hands-on experience behind these answers — not just memorized responses.
