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 hold back large or reasoning models for genuinely hard ones, often with a cascade that escalates on low confidence. AI, ML, and GenAI 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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