AI Interview Question Bank
Curated questions on system design, prompt engineering, RAG, LLM evaluation, and AI agents — 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.
All Questions(25 of 25)
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 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.
Read questionHow Would You Implement Memory for a Long-Running AI Agent?
Design a memory system for a long-running AI agent — covering in-context working memory, episodic recall, semantic knowledge, and retrieval strategies.
Read questionHow Do You Decide What Tools to Give an AI Agent?
A framework for deciding which tools to give an AI agent — covering granularity, safety boundaries, observability, and the principle of minimal tool sets.
Read questionDesign a Conversational AI Customer Support System
Design an AI-powered customer support system that handles common queries automatically while escalating complex issues to human agents.
Read questionDesign a Document Q&A System for a Large Corpus
Design an AI system that answers natural language questions over a large collection of documents, with accurate citations and low hallucination rates.
Read questionHow Do You Build an Eval Suite for an LLM-Powered Feature?
Walk through building a systematic evaluation suite for an LLM feature — from test case design to automated metrics and regression tracking.
Read questionWhat Strategies Do You Use to Reduce Hallucinations?
Walk through a layered approach to reducing LLM hallucinations — from prompt-level techniques to retrieval grounding and output validation.
Read questionHow Would You Design a Prompt for Structured Data Extraction?
Design a prompt that reliably extracts structured data (JSON, tables) from unstructured text — handling missing fields, ambiguity, and format errors.
Read questionHow Do You Handle Chunking Strategies for Different Document Types?
Compare chunking strategies for different document types — PDFs, code, HTML, and tables — and learn when each approach works best.
Read questionDesign a RAG Pipeline from Scratch
Walk through designing a production-ready RAG system covering document ingestion, chunking strategies, embedding models, vector search, and LLM generation.
Read questionHow Would You Evaluate Retrieval Quality in a RAG System?
Walk through metrics and methods for evaluating retrieval quality in a RAG pipeline — from offline metrics to end-to-end answer quality.
Read questionDesign 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 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 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 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 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 questionPrep the coding round too
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