Guardrails Interview Questions for 10 Years Experience

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.

Hard project question on LLM red-teaming — adversarial prompts, automation, severity rubrics, and fix-verify cycles.

Cursor rules are context engineering in disguise.

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.

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.

Hard production LLM question on routing, cascading, and router evaluation.

**Dimensions** Quality, cost curve, latency, compliance, ops burden, roadmap velocity.

**Analysis** Rank features by volume, quality bar, data sensitivity benefiting on-prem.

**Training** Refusal datasets, toxicity filters on training data.

**Flow** Multimodal intake → diagnose → suggest fix → verify resolved.

**Vertex settings** Safety filters on HATE, HARASSMENT, etc. with threshold BLOCK_ONLY_HIGH vs stricter.

**Architecture** LangGraph with interrupt before sensitive tools (lg-004); Bedrock or enterprise API.

**Diagnosis** Categorize refusals: policy vs ambiguous vs bug; replay with rubric.

**Layering** Base model alignment + runtime classifiers + product policies + moderation.

Dangerous tools are why agents aren't auto-root on production.

Hard prompt design on instruction stacking, jailbreak override attempts, and provider-specific precedence rules.

Hard context engineering trade-offs: prioritization tiers, dynamic reallocation, and what to drop first under pressure.

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

Hard AI Agents interview question on long-running agents and checkpoints — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on agent evals with private task suites — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on meta interview: content moderation agents — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on google interview: multi-agent search system — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on multi-user concurrent agents — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on building an internal agent marketplace — architecture, trade-offs, eval, and production patterns.