Interview guides
Deep study guides for production AI interviews — RAG, agents, MCP, evaluation, and system design — each paired with curated questions.
Loading guides...
Browse by topic
49 guides · 298 curated questions
Featured guide
RAG
Advanced RAG interviews test agentic retrieval loops, GraphRAG trade-offs, corrective and self-RAG patterns, and when complexity actually improves answer quality.
AI CareerBehavioral AI interviews test whether you have shipped real systems—production incidents you resolved, cost-quality trade-offs you navigated, and how you communicate uncertainty to stakeholders.
Deep LearningDeep learning production interviews cover training instability, distributed training basics, GPU memory management, and when custom DL beats pre-trained foundation models.
Machine LearningML interviews test when gradient boosting still beats deep learning, how you calibrate probabilities, and how classical models integrate alongside LLM components.
AI FundamentalsAI fundamentals interviews in production roles connect precision-recall trade-offs, data leakage, and bias-variance to real system failures—not undergraduate exam questions.
Computer VisionVision production interviews test inference pipelines, model selection for detection versus classification, content moderation, and monitoring for data drift in visual inputs.
Computer VisionMultimodal RAG interviews cover chart understanding, visual PDF ingestion, cross-modal retrieval, and citation strategies when answers depend on images—not just text chunks.
Model ProvidersProvider interviews test multi-vendor routing, SLA trade-offs, data residency, pricing models, and eval-driven model selection—not brand loyalty.
Production interview guide for LLM cost and latency — caching, routing, streaming, rate limits, and semantic caches.
Interview prep for LLM evaluation — classic metrics limits, LLM-as-judge design, offline vs online eval, and red-teaming.
Observability and evaluation for coding agents — traces, cost/quality metrics, alerts, and private-repo bakeoffs.
Threat model MCP coding agents — prompt injection, malicious servers, .mcp.json attacks, and runtime governance.
Enterprise AI governance for coding agents — approval workflows, least privilege, sandboxes, and AI platform engineering ownership.
AI FrameworksFramework interviews test when to adopt orchestration libraries, how abstractions help or hurt debuggability, and how you avoid framework lock-in on critical paths.
What belongs in AGENTS.md, why concise files win, company-wide templates, and securing agent config against repo attacks.
From prompt engineering to context engineering — budgets, retrieval, compression, and company-wide repository context.
Model Context Protocol interview guide: hosts, clients, servers, agent gateways, and enterprise MCP deployments.
Handbook-style interview prep: why LLMs hallucinate, hallucination types, and production defenses (RAG, guardrails, citations, eval) — asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.
System design for AI coding agents — planner, tools, memory, verifiers, MCP, sandboxes, and a platform for 1,000 engineers.
OpenAI Codex CLI interview guide — when to use a CLI coding agent vs Copilot, plus cost optimization for high-volume agent jobs.
Cursor vs GitHub Copilot interview prep: codebase indexing, agent edits, .cursor/rules, enterprise fit, and review gates.
Interview guide for Claude Code — CLI coding agents, AGENTS.md workflows, sandboxes, and how Claude Code compares to Cursor and Copilot.
Python for AIPython interviews for AI roles test FastAPI service design, async I/O with LLM clients, Pydantic validation, and memory-safe batch processing—not trivia about the GIL.
MLOpsMLOps interviews for GenAI cover prompt CI/CD, model routing changes, drift detection, cost observability, and incident response when quality cliffs appear after deploy.
System DesignSupport system design interviews test ticket retrieval, action tools, human handoff, and how you measure resolution rate without sacrificing safety or CSAT.
System DesignDesign interviews for enterprise RAG platforms test ingestion scale, ACL-aware retrieval, eval infrastructure, and operability across hundreds of document sources.
LLM InferenceInference interviews test continuous batching, KV cache memory math, quantization trade-offs, speculative decoding, and how p99 latency budgets drive architecture.
AI SecurityAI security interviews cover prompt injection, jailbreaks, RAG poisoning, tool abuse, and red-team programs that produce fixes—not just findings slides.
AI EvaluationEvaluation interviews test pipeline design: golden datasets, LLM judges, human rubrics, CI gates, and how online metrics close the loop with offline benchmarks.
MCPMCP security interviews focus on permission models, tool poisoning, prompt injection via resources, and why agent tool access is as sensitive as database credentials.
MCPMCP interviews cover client-server boundaries, gateway patterns, transport choices, and how to scale tool ecosystems without creating undebuggable agent spaghetti.
Multi-Agent SystemsMulti-agent interviews examine supervisor patterns, delegation protocols, shared state consistency, and cost explosion when agents talk too much to each other.
AI AgentsAgent interviews test planner-executor design, tool validation, memory boundaries, and how you prevent infinite loops—not whether you can spell ReAct.
SLMSLM interviews focus on when smaller models beat frontier APIs: latency budgets, on-device privacy, distillation pipelines, and quantization for CPU inference.
Fine-TuningAlignment interviews cover preference data quality, DPO versus PPO trade-offs, reward hacking, and how post-training choices affect downstream agent reliability.
Fine-TuningInterviewers want a decision framework—not a tribal preference. Learn when fine-tuning, RAG, or both wins on freshness, cost, compliance, and evaluability.
Prompt EngineeringProduction prompt engineering is software engineering: versioned templates, eval gates, structured outputs, and defenses against injection—not clever one-shot phrasing.
Vector DatabasesVector DB interviews cover HNSW parameter tuning, hybrid retrieval, metadata filtering at scale, and when to use pgvector versus dedicated vector platforms.
EmbeddingsEmbedding interviews test re-embedding strategy, cosine similarity pitfalls, domain adaptation, and how silent embedding drift destroys retrieval quality overnight.
Vector DatabasesANN search, hybrid retrieval, and scaling to 500M vectors — system design for vector DB interviews.
TransformersTransformer interviews for AI engineers emphasize attention complexity, KV cache behavior, positional encoding trade-offs, and how architectural choices affect inference cost—not hand-waving about self-attention.
LLMsGo beyond transformer trivia. Production LLM interviews test token budgets, decoding behavior, hallucination patterns, and how model limitations shape system architecture.
Chain-of-thought, few-shot, and evaluation strategies — the craft behind reliable LLM outputs.
RAGLearn how to debug RAG regressions by separating retrieval failures from generation drift, building slice-aware eval pipelines, and choosing metrics that actually predict user pain.
State machines, cyclic workflows, and the Model Context Protocol — essential for agent infrastructure interviews.
RAGProduction RAG interviews test whether you can ship retrieval systems that stay grounded under real traffic, tenant isolation, and document churn—not whether you can draw a naive retrieve-then-generate diagram.
Model-based interview questions for every major LLM family — know the trade-offs before you walk in.
Master multi-agent systems, tool use, and agent orchestration — the hottest topic in GenAI hiring loops.
Everything you need to ace RAG interviews — from fundamentals and hallucination mitigation to enterprise pipeline design.