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Core
Constrained and Structured Decoding
Asking a model nicely for JSON sometimes fails; constrained decoding guarantees valid output by masking, at each generation step, every token that would violate a schema or grammar, so only valid continuations can be sampled. It is the reliable way to get JSON, enums, or function-call arguments, and it underpins tool calling. The caveat: it guarantees structural validity, not semantic correctness. Applied-AI interviews probe it because production systems depend on parseable output, and 'just prompt for JSON' breaks at scale.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsHow does constrained / structured decoding force an LLM to emit valid JSON or grammar?→RAG & Agent System DesignHow do you get reliable structured output (JSON / function calls) from an LLM in production?→LLM & GenAI FundamentalsHow does LLM tool calling (function calling) actually work under the hood?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→RAG & Agent System DesignWhat is the Model Context Protocol (MCP), and how do you design good tools for an agent?→LLM & GenAI FundamentalsHow do you design system prompts and prompt templates for production?→
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