Tool Use & Function Calling

This course covers function calling rigorously: schema design, reliability patterns, and orchestrating multiple tools. Learners build tool-using flows that degrade gracefully instead of hallucinating actions.

What this course covers

The course runs across 10 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Foundations of Function Calling — Understand the lifecycle of a tool call, how LLMs process tool definitions, and the message exchange protocol between the user, assistant, and tool.
Designing Robust JSON Schemas — Learn the mechanics of JSON Schema. Craft precise parameter descriptions, use strict types, and define required fields to guide the model's extraction.
Lab: Authoring Schemas for Complex Inputs — Hands-on practice writing schemas for nested objects, arrays, and constrained enums to prevent model formatting errors.
Parsing and Executing Tool Calls Safely — Implement safe parsing of arguments returned by the model, execute the corresponding local code, and format the outputs for model consumption.
Error Handling: Recovering from Invalid Arguments — Build feedback loops that catch JSON parsing failures or invalid parameters and feed the error back to the model for self-correction.
Reliability Patterns: Guardrails and Fallbacks — Implement system-level fallbacks, default behaviors, and human-in-the-loop approvals to ensure safety when tools perform destructive actions.
Multi-Tool Orchestration: Parallel Tool Execution — Configure the agent to request and execute multiple independent tool calls simultaneously, optimizing latency and processing parallel responses.
Multi-Tool Orchestration: Sequential Tool Chains — Manage complex, multi-step dependencies where the output of one tool directly determines the inputs and selection of subsequent tools.
Mitigating Tool Hallucination — Learn advanced prompt engineering and dynamic system-message routing to prevent the LLM from inventing tools or arguments that do not exist.
Capstone Lab: Building a Resilient Multi-Tool Agent — Synthesize schema design, error handling, and sequential orchestration to build a fully functional, production-ready tool-using agent.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

Builders, engineers. The material is pitched at advanced level, and takes roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: Integration track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including code lab, guided lab, branching scenario exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

MCP & Connectors in Depth — A deep course on the Model Context Protocol: how it works, building and consuming MCP servers, and its security model. Learners build a working connector that gives an AI tool safe access to a real system.
Structured Outputs & JSON Mode — A focused course on making model output machine-consumable: schemas, enforcement modes, and validation layers. Learners ship a pipeline where AI output feeds software without human cleanup.
Modern AI Stack for Builders — Master the integration layer of modern AI systems

Frequently asked questions

How long does this course take?

It runs roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-FNC.