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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9 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?
Preventing Private Code Leakage in Coding Agents
Your coding agent has access to proprietary code. How do you stop it leaking in suggestions, tool calls and pull requests?
RAG System Design with Component-Level Evaluation
Design a RAG system and explain how you'd evaluate each component independently.
Autonomous Driving Agent Modeling in Simulation
Design an agent-based system for autonomous driving simulation and validation.
Agentic Workflow for Movie Generation
Design a multi-agent workflow that generates movie content from script to final cut.
The “We Want Fine-Tuning” Customer Ask
A customer insists on fine-tuning Claude for their domain. Find what they need, and decide what to build instead.
The Stalled Pilot at 60% Task Success
A customer pilot is stuck at 60% task success in week one. How do you find what is failing and fix it?
Migrating a Production Agent to a New Model
A customer’s production agent runs on a model that retires next month. Move it to the current model without a regression.
The Regulated Customer: Retention, HIPAA and Residency
A healthcare customer wants to use the Claude API with patient data. What can they use, and what has to change?