model selection
Applied AI interview questions tagged model selection, across every topic.
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Concepts behind "model selection"
The curriculum that explains the ideas these questions test.
Core
Small vs Large Models and RoutingBigger is not always better in production: small models are far cheaper and faster, and for many tasks they are good enough, especially when fine-tuned or given retrieval. The mature pattern is routing, send easy queries to a small/cheap model and reserve large or reasoning models for genuinely hard ones, often with a cascade that escalates on low confidence. Applied-AI interviews probe it because picking and routing models is where most of the cost and latency budget is won or lost.🧠 Foundations of LLMs & GenAISign in
Core
Foundation Model Selection and BenchmarkingFoundation model selection is the disciplined process of choosing across frontier models on capability, cost, latency, and context window, validated by your own task evals rather than public leaderboards. The core skill is reading benchmarks skeptically (contamination, saturation, prompt sensitivity) and designing for provider migration so you are never locked to one vendor. Applied AI interviews probe it because picking a model by leaderboard rank or brand is the fastest way to ship something that is wrong, slow, or expensive for your actual workload.⚙️ System Design for AI in ProductionSign in