Fine-Tuning System Design Interview Questions

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.

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

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.

**Graph structure** draft → auto_checks → legal_subgraph → manager_subgraph → publish; reject edges loop to draft with comments in state.

**State schema** ticket_id, customer_tier, messages, retrieved_docs, proposed_actions, sentiment, escalation_reason, resolution_code.

Hard AI Agents interview question on customer support agent system design — architecture, trade-offs, eval, and production patterns.

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

Scenario question comparing prompting, RAG, and fine-tuning for domain knowledge — freshness, cost, auditability, and when to combine approaches.

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.

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.

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

Cursor rules are context engineering in disguise.

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

MCP multiplies agent power and attack surface.

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

Company-wide context is a platform product.

Repository configuration attacks target the files agents trust.

Governance is becoming the bottleneck — not model IQ.

Treat agent models/prompts/tools like dependencies.

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 RAG pattern question on corrective retrieval and gated web fallbacks.

Scenario interview on defining and communicating hallucination metrics to executives.

Medium Gemini/Vertex question on context caching vs RAG trade-offs.