Prompt Engineering Interview Questions
Prompt engineering questions test whether you can turn a vague ask into reliable model behavior — structure, examples, reasoning, and guaranteed output shape.
Key concepts to know
- Structure a prompt into role, instructions, context, output format, guardrails.
- State the negative space: what NOT to do, and how to handle unknowns.
- Delimit injected content clearly — clarity + injection defense.
- Few-shot examples are the highest-leverage lever after clear instructions.
- Choose diverse examples covering edge cases and the 'none' case; match the exact output format.
- Examples cost tokens on every call — a lot of them hints at fine-tuning/dynamic selection.
- CoT ("think step by step") boosts multi-step accuracy by giving the model a scratchpad.
- Separate reasoning from the final structured answer; extract the answer explicitly.
- Reasoning models think internally — don't force manual CoT; mind the token cost.
- Prefer structured output / schema-constrained generation; else JSON + validate + retry.
- Treat model output as untrusted: parse, validate, fallback.
- Engineer prompts: version control, change one variable, test against an eval + regression set.
Interview questions & answers
Which addition most improves reliability of a classification prompt?
Constraining to a fixed category list, specifying an exact JSON schema, and defining a fallback for uncertainty.
Why: Reliability comes from constraints: fixed label set, explicit output schema, and an uncertainty fallback. Politeness and brevity don't drive correctness; high temperature hurts consistency.
Your extraction prompt gets the format wrong ~20% of the time with zero-shot. Cheapest first fix?
Add a few diverse worked examples (few-shot) showing the exact desired output, including a tricky case.
Why: Few-shot examples are the fast, cheap fix for format adherence. Reach for fine-tuning only when prompting plateaus or example tokens get expensive.
For a multi-step logic task, chain-of-thought helps mainly because:
The generated reasoning tokens act as a scratchpad, letting the model work through intermediate steps before committing to an answer.
Why: CoT gives the model intermediate working space (tokens) to decompose the problem, improving multi-step accuracy. It doesn't change model size, temperature, or add retrieval.
The most robust way to ensure valid structured output is to:
Use schema-constrained/structured output where available, and otherwise parse-validate-retry against a schema.
Why: Schema-constrained generation is strongest; when unavailable, validate against a schema and retry. Regex/hoping/temperature don't guarantee well-formed, valid output.