🏭 Production AI Engineering¶
Staff/Principal-level interview preparation for production AI systems — debugging RAG, optimizing costs, designing enterprise agents, building multi-agent workflows, and securing MCP-based applications.
Overview¶
Production AI engineering bridges the gap between prototype and production. These 15 questions cover the most common failure modes, architectural decisions, and design challenges faced when deploying LLM-powered systems at scale.
Currency
Reviewed against provider docs and the MCP spec as of October 2026. The answers avoid hard-coding model names and prices, which change every few months; where a version-sensitive detail matters (sampling parameters, prompt-caching pricing, MCP revision), it is dated in the text. Check current provider docs before quoting numbers in an interview.
📋 Questions Covered¶
Part I — RAG & LLM Debugging¶
| # | Question | Key Topics |
|---|---|---|
| 1 | RAG hallucinates despite having the right context | Faithfulness diagnosis, lost-in-the-middle, citations, NLI/judge verification |
| 2 | RAG retrieval is too slow on a large knowledge base | HNSW/IVF-PQ/DiskANN, quantization, two-stage retrieval, caching |
| 3 | Model gives confident but wrong answers in high-risk situations | Uncertainty estimation (semantic entropy, self-consistency, log-probs), calibration, abstention, verification chain |
| 4 | RAG fails on multi-document reasoning | Query decomposition, Map-Reduce, Graph RAG, ReAct patterns |
| 5 | PM wants to ship with 15% edge case hallucinations | Risk assessment, guardrail proposals, phased rollout, escalation |
Part II — Production AI Systems Design¶
| # | Question | Key Topics |
|---|---|---|
| 6 | RAG suddenly gives wrong answers | Incident triage, "what changed", version pinning, monitoring |
| 7 | Design a production AI coding assistant | Model routing, code indexing, sandboxing, prompt caching, observability |
| 8 | LLM latency jumps from 2s to 15s | TTFT vs decode, reasoning tokens, rate limits, prefix caching, self-hosted serving levers |
| 9 | Design an enterprise AI agent | Tenant isolation, RBAC, audit logging, human-in-loop, prompt injection, structured outputs |
| 10 | Build a multi-agent workflow | Workflows vs agents, orchestrator-workers, conflict resolution, token cost, single-agent tradeoffs |
| 11 | Same prompt gives different outputs | Temperature, Top-P, Seed, batch-invariance non-determinism, 2026 API restrictions |
| 12 | AI inference costs increased by 40% | Cost attribution, prompt caching, token budgets, routing, batch API |
| 13 | AI assistant works in testing but fails in production | Distribution shift analysis, triage metrics, LLM tracing (OpenTelemetry GenAI) |
| 14 | How to evaluate an LLM in production | Online/offline/safety/cost metrics, LLM-as-judge, hallucination detection without ground truth |
| 15 | Design an enterprise MCP-based AI application | MCP 2026-07-28 (stateless), gateway + OAuth, tool policies, DLP, encrypted memory |
🎯 Target Audience¶
- Staff/Principal Software Engineers preparing for AI engineering interviews
- ML Infrastructure Engineers building production RAG and agent systems
- AI Architects designing enterprise-grade LLM applications
- Engineering Managers evaluating production AI readiness
🔗 Quick Links¶
| Resource | Description |
|---|---|
| 📄 Full Interview Questions & Answers | Complete 15-question guide with code examples and follow-ups |
| 📚 RAG Interview Questions | RAG-specific architecture, chunking, and production scaling |
| 🤖 Agent Interview Questions | Agent architecture, memory systems, and multi-agent coordination |
| 🔌 MCP Interview Questions | MCP protocol, server design, and production deployment |
💡 Key Principle¶
In production AI engineering, the question is never "does it work?" but "how do I know it's working, how quickly can I detect when it stops, and how do I mitigate the impact when it does?"