Applied Scenarios5 Scenarios
Customer-Facing Applied Scenarios
Real-world scenarios from AI engineering interviews — cost-efficient agent routing, code leakage prevention, RAG system design, and multi-agent workflows. Each includes diagnosis, solution architecture, and tradeoff analysis.
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5 scenarios
LLM Inference Cost vs Latency Tradeoffs
You are building an AI-powered product and must choose between a premium model and an efficient model. How would you decide?
Medium
Cost & OptimizationCost OptimizationModel RoutingLatencyProductionGoogleAnthropicOpenAI
Preventing Private Code Leakage in Coding Agents
Your coding agent has access to proprietary code. How do you prevent it from leaking in suggestions?
Hard
Security & PrivacySecurityAgentsCode GenerationGuardrailsGitHubGoogleAnthropic
RAG System Design with Component-Level Evaluation
Design a RAG system and explain how you'd evaluate each component independently.
Hard
RAG & RetrievalSign up to unlockRAGEvaluationArchitectureProductionGoogleAnthropicGlean
Autonomous Driving Agent Modeling in Simulation
Design an agent-based system for autonomous driving simulation and validation.
Hard
Multi-Agent SystemsSign up to unlockAgentsMulti-AgentSafetySimulationGoogleAmazonNVIDIA
Agentic Workflow for Movie Generation
Design a multi-agent workflow that generates movie content from script to final cut.
Hard
Multi-Agent SystemsSign up to unlockAgentsMulti-AgentOrchestrationCreative AIOpenAIGoogleMeta