40 Prompt Engineering Questions (With Interview-Ready Answers)

Prompt engineering isn’t about creating elaborate prompts. It’s about developing the judgment to choose the right structure, logic, and level of control for a given task.

This article gives you 40 scenario-based questions and answers that reflect real decisions you make when working with LLMs in production. Try answering each question before revealing the solution. The explanations focus on why one approach works better than the others in the given scenario.

Q1. A customer support team needs to automatically route incoming tickets into one of four fixed categories: Billing, Technical, Account, or Other. High accuracy and consistency are critical. Which solution is most appropriate?

Q2. A marketing team wants to generate 20 different headline variations for the same product launch to test emotional appeal across audiences. Which AI approach best fits this requirement? 

Q3. A finance department wants to predict next quarter’s revenue using five years of historical transaction data. The output must be numeric and auditable. What is the best approach?

Q4. A startup wants to automate replies to common customer emails like “How do I reset my password?” while still allowing creative responses for open-ended questions. Which strategy is most appropriate?

Q5. A healthcare company is considering using an LLM to determine whether insurance claims should be approved or denied based on strict policy rules. Why is using a Generative AI model risky for this task?

Q6. You want an LLM to summarize customer feedback into exactly three bullet points every time. The model sometimes produces paragraphs instead. Which change is most effective?

Q7. A legal assistant must analyze contracts and always report findings under these criteria: 
– Liability 
– Termination 
– Indemnification 
Which prompting technique best ensures consistency?

Q8. You want an LLM to extract product names, prices, and availability dates from raw text and return them in a predictable structure. What is the best instruction to include?

Q9. An enterprise assistant must never ask users for passwords or sensitive personal information, even if the user explicitly offers it. Where should this rule be enforced?

Q10. A financial services LLM often gives overly confident advice. You want it to sound cautious and compliant without changing the task itself. Which prompt update is most effective?

Q11. An LLM is asked to classify support tickets into three categories. Sometimes it invents new labels not in the allowed list. Which prompt change best prevents this? 

Q12. You want an LLM to follow a specific tone across many interactions: concise, risk-focused, and data-driven. Which approach is most reliable?

Q13. You are testing two prompts for summarizing reports. 

Prompt A: “Summarize this report.” 
Prompt B: “Includes the same instruction plus one high-quality example summary.”

What is the main advantage of Prompt B?

Q14. A regulated enterprise assistant must strictly follow company policies and refuse to answer when information is missing. Which prompt engineering principle best enforce this behavior?

Q15. You want to stabilize inconsistent output formatting from an LLM without rewriting the entire prompt. What is the most effective first step?

Q16. You are calling an LLM API and receive a response object with multiple choices. You want to extract the generated text from the first choice. Which line is correct?

Q17. You have a pandas DataFrame logs_df with columns: 
– user_id 
– request_count
You want to select only users who made more than 50 requests. Which code is correct?

Q18. You are tracking daily snapshots of a nested Python list representing campaign data. To save memory, all snapshots were stored using the same list reference. What will happen when viewing older snapshots?

Q19. You receive a JSON response from an API containing a list of products. You want to analyze prices using pandas. What is the best approach?

Q20. You have a DataFrame api_logs with columns:
– user_id
– tokens_used
You want to calculate total tokens per user, but only for users with more than 10 API calls. Which approach is correct?

api_logs.groupby("user_id")["tokens_used"].sum()

api_logs[api_logs["tokens_used"] > 10].groupby("user_id").sum()

counts = api_logs.groupby("user_id").size()
active = counts[counts > 10].index
api_logs[api_logs["user_id"].isin(active)]
  .groupby("user_id")["tokens_used"].sum()

api_logs.groupby("tokens_used")["user_id"].sum()

Q21. You have a Python list of API responses: 

responses = [ 
{"status": "success", "cost": 0.02}, 
{"status": "error", "cost": 0.00}, 
{"status": "success", "cost": 0.05} 
] 

You want to compute the total cost, but only for successful responses. Which code is correct?

sum(responses["cost"])

total = 0
for r in responses:
    if r["status"] == "success":
        total += r["cost"]

total = responses.cost.sum()

total = 0.02 + 0.05

Q22. You are working with a pandas DataFrame df that contains a column text. You want to add a column word_count that stores the number of words in each row. Which solution is correct?

df["word_count"] = df["text"].apply(lambda x: len(x.split()))

df["word_count"] = len(df["text"])

df["word_count"] = df["text"].apply(len)

df["word_count"] = df["text"].count(" ")

Q23. You are analyzing model latency using a DataFrame metrics with columns:
– status
– latency_ms
You want to flag rows where status is “ok” and latency exceeds 500 ms. Which code is correct? 

metrics["slow"] = metrics["status"] == "ok" and metrics["latency_ms"] > 500

metrics["slow"] = metrics.query("status == 'ok' latency_ms > 500")

metrics["slow"] = metrics["latency_ms"] > 500

metrics["slow"] = (metrics["status"] == "ok") & (metrics["latency_ms"] > 500)

Q24. You have a large pandas DataFrame logs with columns:
– user_id
– response_code
You want a list of unique users who encountered at least one 500 error. Which approach is correct?

logs.loc[logs["response_code"] == 500, "user_id"].unique()

logs.groupby("user_id")["response_code"].count()

logs["user_id"].unique()

logs[logs["response_code"] == 500].count()

Q25. You are calling an LLM API and want to limit the generated output to 100 tokens. Which parameter controls this?

Q26. A GenAI assistant answers policy questions using internal documents. When information is missing, the assistant sometimes guesses. Which design change most effectively prevents this? 

Q27. You are designing an enterprise LLM system that must resist prompt injection attempts while enforcing company tone and compliance rules. Which message-layer design is most secure?

Q28. An AI agent can access a Search tool and a Calculator tool. It sometimes performs calculations before searching for required data. Which prompting strategy best enforces correct tool order?

Q29. A regulated assistant must provide answers that include exact document citations. If multiple sources conflict, it must refuse to answer. Which advanced prompt strategy best enforces this behavior?

Q30. You want an AI agent to explain its reasoning, execute a tool call, evaluate the result, and then continue until the task is complete. Which pattern provides this structured trace?

Q31. An internal AI assistant must generate answers using only a provided knowledge base. If the knowledge base does not contain relevant information, the assistant must respond with: “No information available in company records.” Which principle guarantees this behavior?

Q32. An AI agent receives a complex request that requires multiple dependent steps. You want the agent to decompose the task, validate constraints, and produce a plan before execution. Which prompting approach is best?

Q33. A shopping assistant must always check real-time inventory data before answering availability questions. Which instruction best enforces this behavior?

Q34. You are debugging an agent that uses multiple tools. You need full visibility into each reasoning step, tool call, parameters, and results. Which pattern best supports this requirement?

Q35. An agent must enforce a strict approval workflow: certain actions require validation before proceeding. The agent sometimes skips validation. Which design change is most efficient?

Q36. A compliance assistant must never generate answers that blend internal policy with external assumptions. If required information is missing, it must explicitly refuse. Which prompt engineering principle most directly enforces this?

Q37. An LLM-based code generation system must create correct Python code. To improve accuracy, the model should first internally plan the solution but expose only the final code to users. Which technique best supports this?

Q38. You are designing an orchestration layer where an LLM must convert a user request into a dependency-aware execution plan before calling any tools. Which prompting pattern is most appropriate?

Q39. A customer support agent receives structured tool output and must respond in clear, friendly language without exposing raw system data. Which behavior is most appropriate?

Q40. A security-critical AI agent must enforce a non-negotiable rule: it must never answer questions about employee salaries. Where must this rule be placed to ensure it cannot be overridden?

Score!

If you scored well on these, like more than 30 correct, then you’re already thinking beyond prompts and into system design.

If some questions surprised you, then they’ve served their purpose. As prompt engineering isn’t memorization, it’s judgment. Instead of brute forcing all possible answers to questions, developing a sound understanding of the problem should be the focus.

If you’re finding it too hard to go through the questions you might consider taking a free prompt engineering course.

Vasu Deo Sankrityayan

I specialize in reviewing and refining AI-driven research, technical documentation, and content related to emerging AI technologies. My experience spans AI model training, data analysis, and information retrieval, allowing me to craft content that is both technically accurate and accessible.