AI Interview Questions for Experienced

Hallucination in RAG systems is the #1 production failure mode cited in AI engineering interviews. Your interviewer wants a systematic debugging framework — not a list of buzzwords. Learn how to measure faithfulness, fix retrieval precision, and layer mitigations the way senior engineers at Databricks and Meta actually ship RAG.

Enterprise RAG interviews test system design at scale: ACL-aware retrieval, audit logging, and ingestion pipelines for millions of documents. This is a staff-level question appearing at Microsoft, Salesforce, and Fortune 500 AI teams. Walk through a complete architecture with security boundaries and operational concerns.

Multi-agent orchestration is a senior/staff-level system design question gaining traction at Google DeepMind and Microsoft. Learn the supervisor pattern, shared state management, and how to avoid the 'too many agents' anti-pattern that sinks most candidate answers.

LangGraph has emerged as the go-to framework for stateful agent workflows, replacing brittle LangChain chains in production. Interviewers want graph thinking — nodes, edges, conditional routing, and checkpointing — not just API familiarity. Essential for any LangChain shop hiring GenAI engineers.

Structured JSON output from LLMs is a production necessity at Stripe, Vercel, and every AI-native startup. Interviewers test your reliability engineering — schema enforcement, retry logic, streaming edge cases — not just 'use JSON mode.' Master the full production playbook.

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.

Constitutional AI is Anthropic's differentiator and a must-know for Claude-focused interviews. Go beyond the marketing — explain the self-critique training loop, how CAI compares to RLHF, and practical safety implications for production deployments.

Google's Gemini 1.5 Pro long-context window opens use cases impossible with standard LLMs — whole-codebase analysis, multi-hour video, massive document review. Interviewers test whether you understand real limitations behind the 1M token marketing number.

Self-hosting Llama 3 is a infrastructure-heavy question for ML platform and AI engineer roles at Meta-adjacent companies. Expect deep dives on quantization, vLLM, GPU sizing, and the TCO math that determines build vs buy decisions.

500M vectors at sub-100ms p99 is a staff-level vector search design question from Uber, Airbnb, and large-scale ML platform teams. Learn sharding strategies, index tuning, and the operational trade-offs that separate senior from principal engineers.

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.

A production Claude Code rollout is an agent platform problem, not a 'give everyone a CLI' problem.

Interviewers want a practical comparison, not marketing.

Cursor rules are context engineering in disguise.

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.

A production AI coding agent typically includes:

This is an enterprise platform design question spanning agents, MCP, governance, and observability.

MCP (Model Context Protocol) defines a clean separation:

MCP multiplies agent power and attack surface.

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

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.

Company-wide context is a platform product.

AGENTS.md is becoming the de facto 'README for agents.'