Prompt Engineering Interview Questions: From Basics to Advanced

Prompt Engineering: The Art and Science

As LLMs become central to products, prompt engineering has evolved from a curiosity to a critical skill. Here's how to demonstrate expertise in interviews.

Fundamental Questions

Q: What makes a good prompt?

Strong Answer: "A good prompt is clear, specific, and provides appropriate context. Key elements include:

• Clear task description
• Relevant context and examples
• Specified output format
• Constraints and guardrails
• Handling of edge cases

The best prompts reduce ambiguity while giving the model enough flexibility to handle variations."

Q: Explain zero-shot vs few-shot vs many-shot prompting.

Strong Answer:

• **Zero-shot**: No examples, just instructions. Works for straightforward tasks.
• **Few-shot**: 2-5 examples demonstrating the pattern. Better for complex or nuanced tasks.
• **Many-shot**: 10+ examples. Useful for fine-grained control but uses more context.

Choice depends on task complexity, model capability, and context window constraints.

Q: What is chain-of-thought prompting?

Strong Answer: "Chain-of-thought (CoT) prompting encourages the model to show its reasoning steps before giving a final answer. This improves performance on complex reasoning tasks. You can trigger it with phrases like 'Let's think step by step' or by providing examples that show reasoning. CoT is especially effective for math, logic, and multi-step problems."

Advanced Techniques

Q: Explain the ReAct prompting pattern.

Cover:

• Combines reasoning traces with actions
• Thought → Action → Observation loop
• Enables tool use and grounded reasoning
• Example: "Thought: I need to find the current stock price. Action: search_stock(AAPL). Observation: $185.50..."

Q: What is constitutional AI / self-critique prompting?

Strong Answer: "This technique has the model critique and revise its own outputs according to specified principles. You generate an initial response, then prompt the model to identify problems with that response, then generate an improved version. This improves safety, accuracy, and quality. It's related to how Claude was trained with Constitutional AI principles."

Q: How do you handle context window limitations?

Strategies:

• Summarization of long contexts
• Hierarchical processing (summarize sections, then combine)
• Sliding window approaches
• Selecting most relevant portions
• Using models with longer context (with cost trade-offs)

Practical Prompt Engineering

Q: How do you develop and iterate on prompts?

Strong Answer: "I follow a systematic process:

1. Start with a clear task definition
2. Write initial prompt with basic structure
3. Test on diverse examples including edge cases
4. Analyze failures and categorize error types
5. Iterate: adjust instructions, add examples, add constraints
6. A/B test significant changes
7. Document final prompt with reasoning"

Q: How do you make prompts more reliable?

Techniques:

• Structured output (JSON mode, function calling)
• Output validation and retry logic
• Temperature tuning (lower for consistency)
• Self-consistency (multiple generations, majority vote)
• Breaking complex tasks into steps
• Explicit constraint statements

Q: Show me how you'd prompt for a specific task.

Example task: "Extract structured information from a product review."

Strong approach:

• Define exact output schema
• Provide 2-3 diverse examples
• Handle edge cases (no rating mentioned, multiple products)
• Specify how to handle uncertainty

System Design: Prompt Architecture

Q: How do you manage prompts in production?

Key practices:

• Version control prompts like code
• Separate prompt templates from parameters
• A/B testing infrastructure
• Monitoring prompt performance over time
• Rollback capabilities
• Prompt injection prevention

Q: What is prompt injection and how do you prevent it?

Strong Answer: "Prompt injection is when user input manipulates the model's behavior by including instructions that override the system prompt. Prevention strategies include:

• Input sanitization
• Clear separation between system and user content
• Output validation
• Monitoring for suspicious patterns
• Using models with better instruction following
• Avoiding placing user content before important instructions"

Common Pitfalls

Q: What are mistakes you see in prompt engineering?

• Being too vague in instructions
• Not providing examples for complex tasks
• Ignoring edge cases
• Over-engineering when simple prompts work
• Not testing systematically
• Assuming prompts transfer between models
• Not considering prompt injection risks

Model-Specific Knowledge

Q: How do prompts differ across models?

Key points:

• System message support varies
• Token limits affect strategy
• Some models follow instructions better
• JSON mode availability differs
• Temperature sensitivity varies
• Prompts often need adjustment between providers

Evaluation Questions

Q: How do you evaluate prompt effectiveness?

Metrics:

• Task completion accuracy
• Output format compliance
• Consistency across runs
• Latency and cost
• User satisfaction
• Safety metrics

Methods:

• Automated evaluation with test sets
• Human evaluation for subjective quality
• A/B testing with real users
• Regression testing after changes

Staying Current

Show awareness of:

• New prompting papers and techniques
• Model capability improvements
• Best practices from model providers
• Community-discovered techniques

Prompt engineering is evolving rapidly. The best practitioners combine systematic methodology with continuous experimentation.