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Core
Function Calling and Tool Schemas
Function calling is the protocol behind tool use: you declare tools as JSON schemas, the model emits a structured call (name plus arguments) that your code validates and runs, and the result goes back into the conversation. The hard part is design, not plumbing: tool descriptions and result shapes decide whether the model picks the right tool with the right arguments, and forcing structured output can cost a measurable amount of accuracy. Applied-AI interviews probe it because schema design is where most agents quietly fail.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsHow does LLM tool calling (function calling) actually work under the hood?→RAG & Agent System DesignHow do you get reliable structured output (JSON / function calls) from an LLM in production?→RAG & Agent System DesignWhat is the Model Context Protocol (MCP), and how do you design good tools for an agent?→RAG & Agent System DesignYour agent has many tools but keeps picking the wrong one or passing wrong parameters. How do you fix it?→System Design for AI in ProductionDesign a fraud-detection system that uses LLMs (beyond a classic ML classifier).→ML Infrastructure & GPUsWhat is the serving overhead of structured (JSON/grammar-constrained) output, and how do you cut it?→
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