Core

Small vs Large Models and Routing

Bigger 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.

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RELATED CONCEPTS

Inference-Time Compute and Reasoning Models Prompting vs RAG vs Fine-Tuning Scaling Laws LLM Cost Optimization

PRACTICE THIS IN REAL QUESTIONS

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?→LLM & GenAI FundamentalsExplain tokenization (BPE, WordPiece, SentencePiece) and why it quietly drives cost, latency, and quality.→RAG & Agent System DesignContext windows are now huge. When do you just stuff everything in context instead of building RAG?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→LLM & GenAI FundamentalsExplain Mixture of Experts (MoE): how it works and the training and inference tradeoffs.→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Decagon

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