Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Context & System Prompt Engineering, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Distinguish prompt engineering (single call) from context engineering (multi-step agents)
Explain how system prompts, task constraints, and tool descriptions shape agent behavior
Describe memory management strategies for long-horizon tasks (in-context buffer, summarization, external store)
Explain structured output generation and why constraining model output format matters in production
Topics covered
System prompt basicsPrompt structureSystem prompt designPrompt strategy selectionPrompt vs context engineeringTool descriptions in promptsTask constraintsContext architectureStructured outputUnder-specification failure modeLong-horizon context managementMemory managementAgent behavioral debuggingContext window management
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“How would you explain the difference between a system prompt and a user prompt to someone joining your team for the first time?
“What are the three main memory management strategies for agents that need to track state across many steps, and when would you choose each one?
“When you have an agent that uses RAG to retrieve external documents before answering, how does the structure of the context window change and what prompt design decisions does that force?