OpenAI Prompt Engineer Interview Questions

Chain-of-Thought prompting remains one of the most frequently asked prompt engineering questions, yet candidates often give surface-level answers. Learn when CoT helps vs hurts, production token costs, and advanced variants like self-consistency and tree-of-thoughts that impress senior interviewers.

Prompt engineering focuses on instruction wording. **Context engineering** focuses on the full state fed to the model each step: policies, repository maps, retrieved files, prior tool outputs, memories, and task specs.

Medium multimodal prompting question for UI bug diagnosis.

Medium prompt engineering question on prompt unit tests and CI gates.

Prompt patterns for prohibitions, scoped refusals, alternatives, and measuring over-refusal vs under-refusal.

Programmatic prompt tuning with labeled datasets, composable modules, and optimizers that search instructions and demonstrations.

Hard prompt design on instruction stacking, jailbreak override attempts, and provider-specific precedence rules.

Company-based system design prompt for tool-using assistants with scope limits, confirmation gates, and structured tool policies.

Silent quality decay from model updates, data shift, registry bypass, and stale few-shot banks without version discipline.

Embedding similarity, k-NN example banks, MMR diversity, and eval-driven example curation for few-shot prompts.

Compress agent memory into durable distilled state while preserving goals, constraints, and tool outcomes.

Multi-agent prompt patterns where distinct personas argue, review, or refine outputs before final synthesis.

Protect instructions, secrets, policy, and tool policies in system prompts against leakage, override, and indirect injection.

Build golden sets, error taxonomy, automated graders, and human review loops to iterate prompts like production software.

Context budget tactics: summarization, selective retrieval, example pruning, and distillation for long-running agent sessions.

Evaluate persona and role prompts for tone, domain framing, and when they add noise without improving task accuracy.

MLOps for prompts: semantic versioning, eval gates, traffic splitting, and rollback when prompt changes regress quality or cost.

Advanced reasoning technique: sampling multiple chain-of-thought paths and aggregating answers via majority vote or scoring.

Structured prompting patterns using tags and boundaries to separate instructions, context, tools, and user input in production LLM apps.

Security-focused prompt engineering on jailbreaks, indirect injection, tool sandboxing, and layered defenses for production chatbots and agents.

Foundational prompt engineering question on instruction placement, example selection, and when demonstrations beat bare instructions.

Prompt injection is the #1 security interview topic for agents that read untrusted text.

Advanced scenario question on why LLMs hallucinate — next-token prediction vs fact verification, root causes, and production defenses asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Part 2 of the LLM hallucination handbook — factual, citation, reasoning, math, code, temporal, context, and tool-use hallucinations; decoding risks; RAG failure modes; production case study.