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Core
Foundation Model Selection and Benchmarking
Foundation model selection is the disciplined process of choosing among frontier models on capability, cost, latency, and context window, confirmed by your own task evals rather than public leaderboards. The core skill is reading benchmarks with suspicion (contamination, saturation, prompt sensitivity) and building for provider migration so you are never tied to one vendor. AI, ML, and GenAI engineer 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?→MLOps & ML EngineeringYou need to move a live feature to a different model or provider. How do you cut over without a quality regression?→Machine Learning & Data ScienceTwo models have nearly identical accuracy. How do you decide which one to ship?→
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