Guardrails Interview Questions PDF

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.

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

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

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

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.

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.

**Usage** Separate small model for moderation latency.

**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.

**Eval dimensions** SWE-bench-style tasks on private repo sample; CI fix rate; hallucinated APIs.

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

Database MCP is high risk/high value — interviewers probe your safety instincts hard.

Prompt patterns for prohibitions, scoped refusals, alternatives, and measuring over-refusal vs under-refusal.

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.