OpenAI AI Interview Questions
AI interview questions reported from OpenAI research, applied AI, and platform engineering roles.
How OpenAI AI Interviews Work
OpenAI interviews are known for being technically rigorous and research-leaning even for engineering roles. Typical loops include: systems coding (Python, distributed systems), AI/ML system design, a domain-specific round on LLMs (architecture, training, RLHF), behavioral/culture round, and sometimes a research presentation for senior roles. Expect deep probing on how LLMs actually work.
Key topics to prepare
- LLM architecture (transformers, attention, tokenization)
- RLHF, DPO, and alignment techniques
- Prompt engineering and evaluation at scale
- AI agent design and tool use
- LLM inference optimization (KV cache, batching, quantization)
Interviewer tip
OpenAI expects you to understand LLMs deeply — not just how to use them but how they work. Review transformer architecture, training dynamics, and inference optimization. Be prepared to discuss limitations and failure modes honestly — they value intellectual honesty over confident overstatement.
Prep for the full interview loop
Know the concepts. Now prove it. Practice GenAI, Coding, System Design, and AI/ML Design interviews with an AI that tells you exactly where you fell short.
Questions Asked at OpenAI
Explain the ReAct Pattern and When You Would Use It
Understand the ReAct pattern — how Reasoning + Acting enables LLMs to solve multi-step problems with tools, and when to choose it over alternatives.
Read questionWhat Metrics Would You Track for an LLM in Production?
A comprehensive framework for monitoring LLMs in production — from latency and cost to output quality and user satisfaction signals.
Read questionExplain Chain-of-Thought Prompting and When to Use It
Understand chain-of-thought prompting — how it works, when it helps, and when simpler prompts are actually better.
Read questionHow Do You Evaluate Whether a Prompt Is Working Well?
Walk through a systematic approach to measuring prompt quality — from building eval datasets to automated metrics and human evaluation.
Read questionWhen Would You Choose RAG Over Fine-Tuning?
Understand the tradeoffs between RAG and fine-tuning — and learn a decision framework for choosing the right approach for your use case.
Read questionHow Would You Implement Memory for a Long-Running AI Agent?
Design a memory system for a long-running AI agent — covering in-context working memory, episodic recall, semantic knowledge, and retrieval strategies.
Read questionHow Do You Decide What Tools to Give an AI Agent?
A framework for deciding which tools to give an AI agent — covering granularity, safety boundaries, observability, and the principle of minimal tool sets.
Read questionWhat Is the Plan-and-Execute Agent Pattern, and When Should You Use It Over ReAct?
Plan-and-Execute separates planning from execution in AI agents. Walk through how it works, how it compares to ReAct, and the tradeoffs in multi-step task completion.
Read questionWhat's the Difference Between OpenAI Function Calling and LangChain Agents?
OpenAI function calling and LangChain agents both let LLMs use tools, but they operate at different abstraction levels. Walk through how each works and when to use each.
Read questionHow Do You Build an Eval Suite for an LLM-Powered Feature?
Walk through building a systematic evaluation suite for an LLM feature — from test case design to automated metrics and regression tracking.
Read questionHow Do You Evaluate a RAG System End-to-End?
RAG evaluation is distinct from general LLM evaluation — it requires measuring both retrieval quality and generation quality independently and together. Walk through the key metrics and frameworks.
Read questionWhat Is Prompt Injection, and How Do You Defend Against It?
Prompt injection is one of the most significant security risks in LLM-powered applications. Walk through the attack types and the layered defenses used in production.
Read questionWhat Strategies Do You Use to Reduce Hallucinations?
Walk through a layered approach to reducing LLM hallucinations — from prompt-level techniques to retrieval grounding and output validation.
Read questionDesign a Multi-Agent System for Software Development
Design a multi-agent system where specialized agents collaborate on software development — covering orchestration, communication, coordination, and failure modes.
Read questionDesign a Production LLM Chat System (Design ChatGPT)
Walk through the architecture of a production LLM-powered chat system — covering streaming responses, conversation history management, context window limits, multi-user scaling, and safety.
Read questionHow Would You Detect and Handle LLM Output Regressions?
Build a system to detect when LLM output quality degrades — covering statistical monitoring, automated quality checks, and incident response.
Read questionHow Do You Handle Model Version Upgrades Without Breaking Production?
A safe, systematic approach to upgrading LLM model versions in production — from pre-upgrade evaluation to canary deployment and rollback.
Read questionCompare Few-Shot Prompting vs. Fine-Tuning for a Classification Task
Understand when to use few-shot prompting versus fine-tuning for classification — covering cost, data requirements, latency, and when each approach wins.
Read questionPrep for the full interview loop
Know the concepts. Now prove it. Practice GenAI, Coding, System Design, and AI/ML Design interviews with an AI that tells you exactly where you fell short.
Start a mock interviewFrequently Asked Questions
What does an OpenAI interview look like?▾
OpenAI interviews include systems coding, AI/ML system design, a deep LLM domain round (covering architecture, training, alignment), and a culture/behavioral round. Senior roles may include a research presentation. Expect deep technical probing on how LLMs actually work under the hood.
What AI topics does OpenAI test in interviews?▾
OpenAI tests deep LLM knowledge: transformer architecture, training (pretraining, fine-tuning, RLHF/DPO), inference optimization (KV cache, speculative decoding, quantization), prompt engineering and evaluation, AI agents, and safety/alignment considerations.