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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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TAUGHT IN THESE COURSES
This concept is a reference. These lessons put it in sequence, with what comes before and after it.
Agent Engineering·Tools as an interfaceFree14mDesigning an API for a consumer that cannot read the docsA tool set is an API whose only user is a model with no memory of your codebase and no way to ask. That constraint changes granularity, naming and error design in specific ways, and most tool sets are built as though a person were reading them.Applied AI Engineering·Tools and your first agentSign in13mWhat a tool call actually is, mechanicallyThe single most useful correction for anyone new to tool use: the model never runs anything. It emits a request, your code decides whether to honour it, and that boundary is where all the safety and most of the reliability live.Applied AI Engineering·Tools and your first agentSign in14mTool design is prompt engineering by another nameThe model chooses tools using only their names, descriptions and parameter schemas. That surface is the whole prompt for the decision, which makes tool design an authoring problem rather than an API problem, and explains most wrong-tool bugs.
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?→
COMPANIES THAT ASSUME THIS