B7Beginner 75 minutes reading and codeLocked

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.

In this lesson

  1. 01Learning Objectives
  2. 02The Problem
  3. 03The Concept
  4. 04Build it
  5. 05Use it
  6. 06Exercises
  7. 07Key Terms
  8. 08Further Reading

About 4700 words of reading, with code, 5 quiz questions and the tutor.

The full lesson opens with an access code

You can see what this lesson covers and the milestone it sets. The full text, code, quizzes, the tutor and the AI reviews come with the K850 practice, along with weekly build sessions and code review.

Codes come with the K850 practice. Book a place

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Milestone forThe ZRA Tax Desk

Hand the arithmetic to tools

Build vat_on and turnover_tax as tested functions that read rates from a dated table you sourced from ZRA, and expose them as tools. The model decides when to call them; it never computes a figure itself.

Done when

  • vat_on and turnover_tax with strict schemas and at least 12 unit tests
  • A rate table where every rate has a ZRA source and date
  • In 20 traced questions needing a figure, the model computes nothing itself

Stretch: Add a tool that explains which rate was used and from which document

Reviews open with an access code.

Interview practice

Questions on this topic that AI engineering interviews ask, with the companies reported to ask them. Answer the way you would out loud; Jev scores it and DeepSeek tells you what to add.

Scoring opens with an access code. You can still read the questions and prepare.

B6RAG (Retrieval-Augmented Generation)Next · B8The ZRA Tax Desk

Adapted from open-source work: AI Engineering from Scratch by Rohit Ghumare (MIT, lesson text, code and quizzes); Awesome LLM Apps by Shubham Saboo (Apache-2.0, starter code); AI Engineering Interview Questions by Outcome School (Apache-2.0, interview questions). Capstones, milestones, data packs and Zambian context by Zambrite.

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