How do engineers think through AI implementation decisions? Explore real scenarios, different approaches, and the tradeoffs behind each choice.
When building AI systems, engineers face questions without clear right answers. Each scenario shows how these decisions are approached, the options considered, and why tradeoffs exist.
Note: These scenarios present possible approaches, not exhaustive options or definitive solutions. Real implementations require context-specific decisions and typically blend multiple strategies rather than using single approaches.
You're building a RAG-based chatbot using an LLM API (OpenAI, Anthropic, etc.) to answer employee questions about HR policies, benefits, and procedures. Your focus is on the engineering implementation, not the business case or ML model training.
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You're implementing an automated resume screening system using ML classification APIs to help recruiters manage high application volumes. The system scores candidates and surfaces top matches. Your focus is on the engineering implementation and deployment decisions.
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