Prompt engineer role-specific interview questions

As asked

A support assistant needs conversation history, policy docs, customer metadata, and tool results, but the context window is regularly exceeded. How do you redesign the prompt flow?

Sample answer outline

The answer should treat the context window as a scarce product resource. Keep durable instructions short, summarise long conversation history with provenance, retrieve only task-relevant policy chunks, and pass customer metadata as a compact structured block rather than prose. Tool results should be schema-shaped and pruned to fields the next step needs. Add tests for truncation boundaries because prompt changes can silently push out important context. Candidates often rely on larger context windows instead of deciding what information is actually needed for the task.

Expect these follow-ups

• How do you know a summary has not lost a critical fact?
• What context should never be summarised?
• How would you debug a failure caused by truncation?

As asked

An assistant can search a knowledge base, create support tickets, and refund orders. How do you prompt it to choose tools correctly and avoid harmful actions?

Sample answer outline

Define each tool's purpose, required inputs, and preconditions in compact operational language. Separate read-only tools from state-changing tools and require explicit user confirmation before irreversible actions such as refunds. The prompt should instruct the model to gather missing fields before calling a tool and to explain uncertainty rather than guessing arguments. The application should still enforce permissions and confirmation because prompt instructions are not an authorisation boundary. Weak answers focus on friendly wording and ignore tool contracts, auditability, and recovery from failed calls.

Expect these follow-ups

• What tool call should require confirmation?
• How do you handle a tool returning partial or stale data?
• Where do you enforce refund limits?

As asked

An agent SDK needs short-term task state, long-term user preferences, and retrieval over past work. How do you design memory without making the agent unreliable or creepy?

Sample answer outline

Separate memory types by purpose and retention: task state for the current run, user preferences that are explicit and editable, and retrieval memories with provenance. The agent should not silently infer sensitive attributes or treat every past statement as permanent truth. Store memories with source, timestamp, confidence, scope, and deletion controls, then retrieve only memories relevant to the current task. Evaluation should include stale-memory and wrong-user cases. Weak answers describe a vector store as memory without defining consent, correction, or when not to use recalled information.

Expect these follow-ups

• How does a user inspect and delete memory?
• What memory should expire automatically?
• How do you stop retrieved memory from overriding current user instructions?