AI Interview Question Bank
Curated questions on system design, prompt engineering, RAG, LLM evaluation, and AI agents — sourced from real interviews at Google, Meta, Amazon, and more. With walkthroughs, follow-ups, and the kind of detail that actually helps you prep.
Browse by Category
AI Agents & Tool Use
Autonomous AI agents, function calling, planning architectures, and multi-agent systems.
AI System Design
End-to-end design of AI-powered systems — from architecture to deployment.
LLM Evaluation & Ops
Testing, monitoring, and operating LLMs reliably in production environments.
Prompt Engineering
Designing, evaluating, and optimizing prompts for real-world LLM applications.
RAG & Retrieval
Retrieval-Augmented Generation architectures — combining search with LLMs for grounded, accurate AI.
Browse by Company
View all →AI interview questions reported from Google AI, DeepMind, and Cloud AI engineering roles.
Meta
AI interview questions reported from Meta AI, FAIR, and GenAI engineering roles.
Microsoft
AI interview questions reported from Microsoft Copilot, Azure OpenAI, and AI platform engineering roles.
Amazon
AI interview questions reported from Amazon AWS Bedrock, Alexa AI, and GenAI engineering roles.
OpenAI
AI interview questions reported from OpenAI research, applied AI, and platform engineering roles.
NVIDIA
AI interview questions reported from NVIDIA AI inference, GPU computing, and LLM platform roles.
Anthropic
AI interview questions reported from Anthropic research, safety, and applied AI engineering roles.
All Questions(15 of 39)
Design an AI Agent That Can Book Travel End-to-End
Design a multi-step AI agent that books flights, hotels, and transportation — covering tool design, planning loops, error recovery, and user confirmation.
Read questionDesign a Multi-Agent System for Software Development
Design a multi-agent system where specialized agents collaborate on software development — covering orchestration, communication, coordination, and failure modes.
Read questionDesign an AI-Powered Code Review System
Design a system that uses LLMs to automatically review pull requests — identifying bugs, style issues, and suggesting improvements at scale.
Read questionDesign a Real-Time Content Moderation Pipeline Using LLMs
Design a scalable content moderation system that uses LLMs to detect harmful content in real time while minimizing false positives and latency.
Read questionDesign a Production LLM Chat System (Design ChatGPT)
Walk through the architecture of a production LLM-powered chat system — covering streaming responses, conversation history management, context window limits, multi-user scaling, and safety.
Read questionHow Would You Architect a Multi-Model AI Gateway?
Design a unified gateway that routes requests across multiple LLM providers, handles fallbacks, enforces rate limits, and tracks costs per team.
Read questionHow Do You Architect a Multi-Tenant LLM Deployment with Role-Based Data Access?
Enterprise AI products serve multiple customers from shared infrastructure. Walk through how to design tenant isolation, role-based access control, and data governance for a multi-tenant LLM deployment.
Read questionHow Would You Detect and Handle LLM Output Regressions?
Build a system to detect when LLM output quality degrades — covering statistical monitoring, automated quality checks, and incident response.
Read questionHow Do You Optimize LLM Inference for Higher Throughput and Lower Latency?
Walk through the key techniques for optimizing LLM inference performance in production — KV cache management, quantization, continuous batching, and speculative decoding.
Read questionHow Do You Handle Model Version Upgrades Without Breaking Production?
A safe, systematic approach to upgrading LLM model versions in production — from pre-upgrade evaluation to canary deployment and rollback.
Read questionCompare Few-Shot Prompting vs. Fine-Tuning for a Classification Task
Understand when to use few-shot prompting versus fine-tuning for classification — covering cost, data requirements, latency, and when each approach wins.
Read questionA Client's RAG System Has Poor Retrieval Accuracy — How Do You Fix It?
A RAG-based system isn't returning accurate results. Walk through a systematic process to diagnose the root cause and improve retrieval quality.
Read questionDesign a Hybrid Search System Combining Semantic and Keyword Search
Design a search system that combines dense vector search with sparse keyword search — outperforming either approach alone through intelligent score fusion.
Read questionHow Do You Handle Multi-Hop and Multifaceted Queries in a RAG System?
Single-shot retrieval breaks down for complex questions that require reasoning across multiple documents. Walk through strategies to handle multi-hop and multifaceted queries.
Read questionHow Do You Choose a Vector Index and Vector Database for a RAG System?
Compare vector index types — HNSW, IVF, PQ, LSH — and explain how to choose the right vector database given scale, latency, filtering, and cost requirements.
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