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Core
Mixture-of-Experts
A Mixture-of-Experts model replaces the dense feed-forward layer with many expert networks and a router that sends each token to only a few of them. This decouples total parameters (capacity) from per-token compute: the model can be huge while each token activates only a slice. The trade-offs are routing complexity, memory (all experts must be loaded), and load balancing. Applied-AI interviews probe it because most frontier models are MoE, and it explains how models get more capable without proportionally more inference cost.
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LLM & GenAI FundamentalsExplain Mixture of Experts (MoE): how it works and the training and inference tradeoffs.→ML Infrastructure & GPUsWhat makes Mixture-of-Experts models hard to train, and how do you handle routing, load balance, and all-to-all?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→System Design for AI in ProductionDesign an LLM gateway in front of multiple model providers (routing, caching, fallback, rate limits, observability).→LLM & GenAI FundamentalsWhat do the feed-forward (MLP) layers in a transformer do, and why are they most of the parameters?→RAG & Agent System DesignHow do you optimize a RAG or agent system for cost and latency in production?→
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