Prompt Engineering Interview Questions

Prompt engineering is the practice of designing instructions, context, examples, and output constraints so an LLM performs a task reliably.

A good prompt defines the goal, audience, constraints, relevant context, and expected output format. In interviews, strong answers focus on repeatability, evaluation, and failure modes rather than clever wording alone.

Zero-shot prompting gives only instructions, while few-shot prompting includes examples of the desired input-output behavior.

Few-shot examples help the model infer style, structure, edge cases, and label definitions. They are useful when the task is ambiguous or format-sensitive, but they consume context and can bias the model.

System prompts should define stable behavior and boundaries, while user prompts should carry task-specific input.

Separating instruction layers improves control and security. System instructions can set role, safety rules, tool rules, and output requirements, while user content should be treated as untrusted data.

Use a clear schema, constrained instructions, examples, and validation after generation.

For production systems, prompts should describe the target shape and allowed values, but application code should still validate the result. JSON schemas, typed parsers, retries, and fallback handling make structured output more reliable.

Keep only relevant context, order it clearly, remove noise, and tell the model how to use it.

Long prompts can dilute important instructions. Good context management includes summarization, chunk selection, recency control, source labeling, and explicit rules for handling missing or conflicting information.

Prompt injection is malicious or conflicting input that tries to override the intended instructions.

Defenses include treating retrieved and user-provided text as data, separating instructions from content, limiting tool permissions, validating outputs, logging decisions, and using guardrails for high-risk flows.

Evaluate prompts with representative test cases, expected outputs, failure cases, human review, and production metrics.

Prompt quality should be measured, not guessed. Useful metrics include task success, factuality, format validity, refusal correctness, latency, cost, and regression rate across prompt versions.

Prompts should be versioned so teams can track behavior changes, reproduce outputs, and safely roll back regressions.

In production, a prompt is part of the application logic. Versioning prompts with model settings, evaluation results, and release notes makes debugging and compliance easier.