OpenAI AI Architect Interview Questions

Part 3 of the hallucination handbook — production defense layers from retrieval and prompting through guardrails, citations, confidence scoring, eval frameworks, and enterprise architecture. Asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Medium AI Agents interview question on openai swarm / agents sdk patterns — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on browser agents and computer use risks — architecture, trade-offs, eval, and production patterns.

Hard scenario on LLM cost control — token budgeting, routing, caching, batch APIs, and unit economics at millions of queries.

Cost optimization for coding agents is context engineering + runtime policy.

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

Treat agent models/prompts/tools like dependencies.

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.

Hard RAG pattern question on corrective retrieval and gated web fallbacks.

Scenario interview on defining and communicating hallucination metrics to executives.

Medium vector DB / embeddings trade-off question on dimensions vs cost/quality.

Medium multimodal prompting question for UI bug diagnosis.

Easy AI Agents interview question on the ReAct pattern — Thought/Action/Observation loops, trade-offs, and production guardrails.

Hard RAG interview question on evaluating rag: faithfulness, context precision, recall — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on chunking strategies for different document types — architecture, trade-offs, eval, and production patterns.

Scenario question on cross-model tokenizer bugs — chunk boundaries, context overflow, and embedder vs LLM alignment.

Hard scenario on agent memory — episodic vs semantic memory, vector stores, summarization, and model parametric limits.

Scenario question comparing CoT prompting with calculators, code interpreters, and search tools for reliable reasoning.

Hard scenario on jailbreaks — DAN, prompt injection, alignment limits, red-teaming, and layered defenses.

Scenario question on resilient LLM clients — exponential backoff, 429 handling, idempotency keys, and duplicate-safe writes.

Scenario question on LLM guardrails — prompt injection defense, output validation, NeMo/Guardrails patterns, and policy layers.

Scenario question on LLM latency — prefill vs decode, retrieval overhead, streaming, caching, and model routing fixes.

Scenario question on context windows — token budgets, lost-in-the-middle bias, and why RAG still matters despite 1M-token claims.