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What You'll Learn

Design reliable function schemas — naming
types
descriptions — that LLMs can consistently and accurately call in production systems,Build multi-tool orchestration and agentic tool-chaining workflows that handle complex
multi-step tasks reliably,Implement robust structured output validation
error handling
and security safeguards for tool-calling applications,Apply production-grade testing
monitoring
and deployment practices to ship function-calling systems with confidence

Requirements

You should have basic familiarity with how LLMs work (prompts
completions) and some general programming or API experience is helpful for following the technical scenarios
though you wont write code during this course. No prior experience with function calling
tool use
or agentic systems is required — this course builds those concepts from the ground up
starting with schema fundamentals before progressing to advanced orchestration and production patterns.

Description

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.

Who this course is for:

This course is for engineers and AI practitioners who want to build reliable
production-grade function-calling and tool-use systems with LLMs — whether youre adding your first function-calling feature to an application
designing multi-tool agentic workflows
or hardening an existing system for real-world deployment. Its especially useful if youve experimented with basic function calling but want a systematic
evidence-based framework for handling the harder problems: tool selection at scale
structured output reliability
security against prompt injection
and the testing and monitoring discipline that production systems actually require.