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(7 of 39)
Explain the ReAct Pattern and When You Would Use It
Understand the ReAct pattern — how Reasoning + Acting enables LLMs to solve multi-step problems with tools, and when to choose it over alternatives.
Read questionExplain the Tradeoffs Between Latency, Cost, and Quality in LLM Selection
Navigate the three-way tradeoff between LLM latency, cost, and quality — and learn how to make the right selection for different use cases.
Read questionWhat Metrics Would You Track for an LLM in Production?
A comprehensive framework for monitoring LLMs in production — from latency and cost to output quality and user satisfaction signals.
Read questionExplain Chain-of-Thought Prompting and When to Use It
Understand chain-of-thought prompting — how it works, when it helps, and when simpler prompts are actually better.
Read questionHow Do You Evaluate Whether a Prompt Is Working Well?
Walk through a systematic approach to measuring prompt quality — from building eval datasets to automated metrics and human evaluation.
Read questionWhat Are LLM Decoding Strategies, and When Do You Use Each?
Explain how LLMs select output tokens — covering temperature, top-k, top-p nucleus sampling, greedy decoding, and stopping criteria — and when each strategy is appropriate.
Read questionWhen Would You Choose RAG Over Fine-Tuning?
Understand the tradeoffs between RAG and fine-tuning — and learn a decision framework for choosing the right approach for your use case.
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