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Core
Foundation Model Selection and Benchmarking
Foundation 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.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
System Design for AI in ProductionDesign an LLM gateway in front of multiple model providers (routing, caching, fallback, rate limits, observability).→LLM & GenAI FundamentalsHow do you evaluate an LLM, and why are benchmarks and LLM-as-judge both unreliable?→LLM & GenAI FundamentalsWhat are small language models (SLMs) and reasoning models (LRMs), and when do you choose each?→LLM & GenAI FundamentalsHow do you decide between an open-source (self-hosted) LLM and a closed-source API model?→Machine Learning & Data ScienceTwo models have nearly identical accuracy. How do you decide which one to ship?→Machine Learning & Data ScienceFor a dataset with a million points, would you use a deep network or KNN, and why?→
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