Function calling is what lets an LLM stop just talking and start doing — retrieving live data, triggering real actions, and orchestrating multi-step tasks through connected tools. But most engineers learn it through scattered tutorials that stop at a basic single-function demo. This course goes far deeper.

Through 600 scenario-based practice questions across six comprehensive tests, you'll build a working, production-level understanding of what it actually takes to design, orchestrate, secure, and ship reliable function-calling systems.

You'll cover:

Function Calling Fundamentals & Schema Design — JSON schema design, parameter types, naming conventions, and required vs. optional fields
Tool Selection & Multi-Tool Orchestration — disambiguation, routing architecture, and combining results across multiple tools
Structured Output & Parameter Validation — type validation, malformed output handling, and constrained generation techniques
Agentic Workflows & Tool Chaining — state management, task decomposition, self-correction, and multi-step failure handling
Error Handling, Reliability & Security — retries, timeouts, prompt injection defenses, and access control for tool execution
Production Patterns — testing strategy, observability, API design, deployment practices, and cost management at scale

Every question comes with a full explanation, reinforcing one consistent theme: added complexity should always earn its place through genuine, demonstrated value — never adopted just because it sounds sophisticated. Whether you're adding your first function-calling feature, building multi-tool agentic systems, or hardening an application for production, this course gives you the systematic judgment to make sound architectural decisions.