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Interview Q&A · 22 questions · 5 answered publicly

Prompt Engineering Interview Questions

Ordered by how often they're asked. Sign in free to see every answer, rate your confidence and run timed mock interviews.

  1. Most asked Prompt leaf: What makes a good system prompt?

    Summary

    Clear role, constraints, output format, tool policy, and refusal rules.

    Longer Answer

    Clear role, constraints, output format, tool policy, and refusal rules. Keep stable; put volatile context in user/messages.

    Key Points

    • Clear role, constraints, output format, tool policy, and refusal rules
    • Keep stable; put volatile context in user/messages
    • Prompt foundations
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Prompt leaf: What makes a good system prompt?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Clear role, constraints, output format, tool policy, and refusal rules.
    Practical tradeoff Prompt foundations.

    Permalink

  2. Most asked Prompt leaf: How do few-shot examples help?

    Summary

    Demonstrate desired format and edge cases.

    Longer Answer

    Demonstrate desired format and edge cases. Prefer diverse, labeled good/bad examples; avoid contradictory shots.

    Key Points

    • Demonstrate desired format and edge cases
    • Prefer diverse, labeled good/bad examples; avoid contradictory shots
    • Few-shot technique
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Prompt leaf: How do few-shot examples help?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Demonstrate desired format and edge cases.
    Practical tradeoff Few-shot technique.

    Permalink

  3. Most asked Prompt leaf: Structured outputs vs free text?

    Summary

    Prefer JSON schema/constrained decoding for APIs.

    Longer Answer

    Prefer JSON schema/constrained decoding for APIs. Free text for narrative; parse carefully with validation.

    Key Points

    • Prefer JSON schema/constrained decoding for APIs
    • Free text for narrative; parse carefully with validation
    • Production interfaces
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Prompt leaf: Structured outputs vs free text?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Prefer JSON schema/constrained decoding for APIs.
    Practical tradeoff Production interfaces.

    Permalink

  4. Most asked Prompt leaf: How do you mitigate prompt injection?

    Summary

    Delimit untrusted input, ignore instructions inside data, allowlist tools, and validate outputs for exfiltration.

    Longer Answer

    Delimit untrusted input, ignore instructions inside data, allowlist tools, and validate outputs for exfiltration. Security.

    Key Points

    • Delimit untrusted input, ignore instructions inside data, allowlist tools, and validate outputs for exfiltration
    • Security
    • Relevant areas: Ai, Prompt Engineering, Security
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Prompt leaf: How do you mitigate prompt injection?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Delimit untrusted input, ignore instructions inside data, allowlist tools, and validate outputs for exfiltration.
    Practical tradeoff Security.

    Permalink

  5. Most asked Prompt leaf: How do you reduce hallucinations via prompting?

    Summary

    Require citations, allow 'I don't know', constrain to provided context, and ask for uncertainty estimates.

    Longer Answer

    Require citations, allow 'I don't know', constrain to provided context, and ask for uncertainty estimates. Grounding prompts.

    Key Points

    • Require citations, allow 'I don't know', constrain to provided context, and ask for uncertainty estimates
    • Grounding prompts
    • Relevant areas: Ai, Prompt Engineering, Grounding
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Prompt leaf: How do you reduce hallucinations via prompting?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Require citations, allow 'I don't know', constrain to provided context, and ask for uncertainty estimates.
    Practical tradeoff Grounding prompts.

    Permalink

  6. Prompt leaf: How do you version and test prompts?
  7. Prompt leaf: When is chain-of-thought useful?
  8. Prompt leaf: Role of delimiters and XML-ish tags?
  9. Prompt leaf: What is constitutional / policy prompting?
  10. Prompt leaf: Tool-choice prompting strategies?
  11. Prompt leaf: How do you design refusal behavior?
  12. Prompt leaf: What is prompt chaining?
  13. Prompt leaf: How do temperature and top_p affect answers?
  14. Prompt leaf: Evaluating prompt changes without users?
  15. Prompt leaf: How do you keep prompts free of secrets?
  16. Prompt leaf: Dynamic prompts with retrieved context-ordering tips?
  17. Prompt leaf: How do you handle multilingual prompts?
  18. Prompt leaf: Why put critical constraints near the end of long prompts?
  19. Prompt leaf: What is self-consistency decoding?
  20. Prompt leaf: How do style guides enter prompts?
  21. What prompt engineering techniques improve reliability of production LLM features?
  22. How do you defend against prompt injection in an application that uses retrieved documents or user content?