Roadmap to LLM Engineer

RAG has become the foundational architecture for production GenAI applications at companies like Notion, Duolingo, and Morgan Stanley. Interviewers expect you to explain the full retrieval pipeline — not just define the acronym. Follow along to master what RAG is, when to use it over fine-tuning, and how to articulate trade-offs that separate junior from senior candidates.

AI Agents are the hottest topic in 2025–2026 GenAI interviews, but most candidates confuse agents with chatbots. Interviewers at OpenAI and Anthropic want you to articulate the agent loop — perceive, plan, act, reflect — and explain when tool use justifies agent complexity over a simple chain.

The Model Context Protocol is Anthropic's open standard reshaping how LLMs connect to tools and data. If you're interviewing for Claude ecosystem roles or AI platform engineering, expect MCP questions. Understand host vs server architecture and how MCP differs from ad-hoc function calling.

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.

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.

A production AI coding agent typically includes:

AI observability extends classic APM.

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.

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.

Scenario interview on defining and communicating hallucination metrics to executives.

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

Hard Llama/inference question on continuous batching and GPU efficiency.

Hard orchestration comparison — LangGraph agent graphs vs durable workflow engines.

Medium prompt engineering question on prompt unit tests and CI gates.

Model-based interview on DPO vs RLHF — preference pairs, training simplicity, and production trade-offs.

Scenario interview on safe GenAI rollouts — flags, shadow eval, canaries, and kill switches.

LLM interview topic on distillation — teacher/student training, latency/cost wins, and quality regression risks.

**Metrics** Requests queued, p95 latency, GPU memory headroom.

**Training** Rank selection, learning rate, epochs; validation loss + task evals.

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

**Eval design** Expert-labeled scenarios; citation accuracy checks; refusal appropriateness.

**Structure** Eval templates + data JSONL + scoring functions; CI integration via API.

**Unit layer** Pure functions: conditional edges, state merge logic, parsers — no graph compile needed.