Tokenization Interview Questions for 10 Years Experience

Cursor rules are context engineering in disguise.

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 Llama/inference question on continuous batching and GPU efficiency.

**vLLM** High throughput continuous batching; popular for multi-tenant APIs.

MCP auth interviews test whether you treat agents as privileged automation — because they are.

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

Hard AI Agents interview question on agent permissions and least privilege — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on multi-agent debate and critique patterns — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on self-rag and corrective rag — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on rag latency budget: sub-second design — architecture, trade-offs, eval, and production patterns.

Hard Meta company question — Llama serving, GPU clustering, quantization, batching, and open-weight ops.

Hard model question on MoE — routed FFN experts, sparse compute, capacity factors, and Mixtral/GPT-4 class rumors.

Hard scenario on PII in LLM systems — detection, redaction, DPA compliance, log minimization, and regional deployment.

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

Foundational model question on BPE/tokenizers — why token counts differ from words, and production impact on cost, limits, and RAG chunking.

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.

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.

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

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

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

MCP multiplies agent power and attack surface.