AI Engineering/Phase 11/Lesson 09

Function Calling & Tool Use

LLMs cannot do anything. They generate text. That is the entire capability. They cannot check the weather, query a database, send an email, run code, or read a file. Every "AI agent" you have ever seen is an LLM generating JSON that says which function to call -- and then your code actually calling it. The model is the brain. Tools are the hands. Function calling is the nervous system connecting them.

Build 1.3 hr Python

Prerequisites: Phase 11 Lesson 03 (Structured Outputs)

#Learning Objectives

Implement a function calling loop: define tool schemas, parse the model's tool-call JSON, execute functions, and return results
Design tool schemas with clear descriptions and typed parameters that the model can reliably invoke
Build a multi-turn agent loop that chains multiple function calls to answer complex queries
Handle function calling edge cases: parallel tool calls, error propagation, and preventing infinite tool loops

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.