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Top 37 Prompt Engineering Interview Questions
Entry Junior Mid Senior Expert
Prompt Engineering Theoretical Questions
Q1:
What is prompt engineering?
Entry
Q2:
What is the difference between zero-shot and few-shot prompting?
Entry
Q3:
What does temperature control in an LLM API call?
temperature control in an LLM API call? Entry
Q4:
What is the difference between a system prompt and a user prompt?
Entry
Q5:
What is negative prompting, and why is it less reliable than positive instruction?
Junior
Q6:
What is in-context learning?
Junior
Q7:
What is chain-of-thought (CoT) prompting?
CoT) prompting? Junior
Q8:
What is a context window, and why does it constrain prompt design?
Junior
Q9:
What is the difference between top-p and top-k sampling?
top-p and top-k sampling? Junior
Q10:
How would you use XML tags or delimiters to structure a prompt?
XML tags or delimiters to structure a prompt? Junior
Q11:
What is zero-shot chain-of-thought prompting?
Junior
Q12:
How does role prompting change a model's output?
Junior
Q13:
What is the ReAct prompting pattern?
Mid Q14:
What is self-consistency prompting?
Mid Q15:
Why explain the rationale behind a prompt constraint?
Mid Q16:
What is prompt chaining?
Mid Q17:
How does prompt injection differ from jailbreaking?
Mid Q18:
How does prompting differ for function calling / tool use?
Mid Q19:
What is meta-prompting?
Senior Q20:
What is LLM-as-a-judge, and what are its pitfalls?
Senior Q21:
What is least-to-most prompting?
Senior Q22:
What is Tree of Thoughts (ToT) prompting?
ToT) prompting? Senior Q23:
How does prompting differ for extended-thinking / reasoning models?
Senior Q24:
What is automatic prompt optimization (e.g. DSPy, APE)?
DSPy, APE)? Expert Q25:
Why can chain-of-thought reasoning be unfaithful to a model's real computation?
Expert Q26:
Why is prompt output brittle to small wording changes, and how do you guard against it?
Expert Prompt Engineering Practical Challenges
Q1:
How would you force a model to return an exact output format, like 3 bullet points?
Junior Q2:
How do you write clear and direct prompts?
Junior Q3:
How would you design effective few-shot examples?
Mid Q4:
What is prefilling a model's response, and when would you use it?
Mid Q5:
How would you reduce hallucinations through prompt design?
Mid Q6:
How would you prompt a model to reliably return valid JSON?
JSON? Mid Q7:
How does prompt caching reduce cost and latency?
Mid Q8:
What is prompt injection, and how would you mitigate it?
Senior Q9:
How would you design an eval set to test a prompt change before shipping it?
Senior Q10:
How would you prompt a model to self-check its answer?
Senior Q11:
How would you set a tool-use policy in a prompt?
Senior Prepare for prompt engineering interviews with answered LLM prompting, ChatGPT prompting, and Claude prompting interview questions covering context windows, few-shot learning, structured outputs, tool use, prompt caching, hallucination reduction, eva...
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Scala is a powerful language with functional programming capabilities that can be a good choice for data science, especially in big data and distributed computing scenarios. As an example, Apache Spark, a popular distributed data processing framework...
PyTorch popularity as a Deep Learning framework of choice is on the rise. As of December 2022, 62% of the academic papers were implemented in PyTorch whereas only 4% were for TensorFlow. Follow along and prepare effectively with these key 30 PyTorch ...
The use of Artificial Intelligence (AI) in machine learning and data science enabled advancements in areas such as natural language processing, computer vision, recommendation systems, fraud detection, predictive analytics, and personalized medicine....
MLStack.Cafe is a biggest hand-picked collection of top Machine Learning, Data Science & Python Interview Questions. Land your next Six-Figure DS job offer in no time.
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