How to Crack the OpenAI Interview

OpenAI's model lineup changes fast. GPT-4 vs GPT-4o is a model selection question that tests whether you understand latency, cost, multimodal capabilities, and when reasoning depth matters. Critical for any role touching OpenAI APIs in production.

Codex CLI represents OpenAI's push into agentic coding outside the editor — similar category to Claude Code and other CLI agents. The core loop is: understand task → explore repo → edit → run commands → iterate.

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

Medium GPT API question on structured outputs reliability vs JSON mode.

**Storage** Vector + structured facts table keyed by user_id; version memories on conflict.

**Proxy layer** Backend holds keys; assigns budgets per customer/plan.

**Azure benefits** VNet, Azure AD auth, Microsoft compliance certifications, existing EA billing.

**Contractual** Enterprise DPA, ZDR endpoints, prohibition on training customer data.

**Session** Persistent realtime connection with session.update for instructions/tools; handle reconnect.

**Use cases** Where zero-shot vision fails consistently on proprietary visual patterns.

**Decision** Try prompt + tools + RAG first; fine-tune if style/reliability gap persists and data exists.

**Structure** Eval templates + data JSONL + scoring functions; CI integration via API.

**Pipeline** Pre-filter user input → model → post-filter output before display; async moderation for streaming with revoke.

**Model selection** Benchmark retrieval on your corpus — smaller models cheaper if recall OK.

**Fit** Nightly evals, backfill embeddings, document tagging — not interactive chat.

**Model behavior** o-series and GPT-4.1 emphasize extended internal reasoning before answer — improved reliability on complex tasks, not always needed for chat.

**Common failures** Model calls nonexistent tool; args wrong type; parallel tool_calls partial failure; streaming partial JSON; user content tricks model into exfil tool args.

**Concept** Responses treat interaction as structured items (messages, tool calls, outputs) with native support for reasoning models and built-in tools — reducing glue code.

**Assistants API** Managed threads, code interpreter, file search, function tools with run lifecycle (queued/in_progress). Less boilerplate for CRUD-style agent apps. Trade-offs: vendor lock-in, run polling complexity, harder fine-grained graph control.

RAG has become the foundational architecture for production GenAI applications at companies like Notion, Duolingo, and Morgan Stanley. Interviewers expect you to explain the full retrieval pipeline — not just define the acronym. Follow along to master what RAG is, when to use it over fine-tuning, and how to articulate trade-offs that separate junior from senior candidates.

AI Agents are the hottest topic in 2025–2026 GenAI interviews, but most candidates confuse agents with chatbots. Interviewers at OpenAI and Anthropic want you to articulate the agent loop — perceive, plan, act, reflect — and explain when tool use justifies agent complexity over a simple chain.

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.

Claude Code is Anthropic's CLI coding agent designed for repository-level work: reading the codebase, editing multiple files, running tests/builds, and iterating until a task succeeds. Unlike autocomplete-first copilots that live inside the editor, Claude Code operates as an agent loop — observe → plan → act (edit/run tools) → verify.

Interviewers want a practical comparison, not marketing.