ChatGPT: Mastery — Building with the API

This course teaches developers to build on the OpenAI API. It covers chat completions, function calling, assistants, embeddings, and cost management. Learners leave able to integrate OpenAI models into real applications with structured, reliable output.

What this course covers

The course runs across 12 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.

OpenAI API Basics and Authentication — Understand the core architecture of the OpenAI API, set up developer accounts, manage API keys securely, and make your first successful API request using Python.
Mastering Chat Completions — Deep dive into the Chat Completions API. Learn how to structure system, user, and assistant messages, configure hyperparameters like temperature and top_p, and handle streaming responses.
Structuring Reliable JSON Outputs — Implement JSON Mode and Structured Outputs to enforce strict schema adherence, ensuring your application receives reliable, machine-readable data from the LLM.
Implementing Function Calling: Basics — Learn the mechanics of function calling. Define custom tools using JSON schemas and parse the model's tool call requests in your application code.
Advanced Function Calling and Tool Execution — Build end-to-end execution loops where your code runs the requested functions, sends the results back to the model, and retrieves final natural language responses.
Introduction to the Assistants API — Explore the Assistants API architecture. Understand Threads, Runs, and Messages, and learn how OpenAI manages conversation state automatically.
Equipping Assistants with Code Interpreter and File Search — Enable advanced capabilities in your Assistants. Learn how to upload files, activate the Code Interpreter for data analysis, and configure File Search for vector-based retrieval.
Working with Text Embeddings — Generate vector embeddings using OpenAI's embedding models. Understand cosine similarity and how embeddings form the foundation of semantic search.
Building a Basic Retrieval-Augmented Generation (RAG) Pipeline — Combine embeddings, a local vector database context search, and Chat Completions to build a functional, grounded QA system over custom documents.
API Error Handling and Rate Limit Mitigation — Implement robust error-handling strategies, exponential backoff, and queuing systems to gracefully handle rate limits (TPM/RPM constraints) and API outages.
Cost Management and Token Optimization — Analyze token consumption using tiktoken. Learn cost-saving strategies including prompt engineering optimization, choosing cost-efficient models, and utilizing API caching.
Monitoring, Security, and Production Best Practices — Establish monitoring for API costs and latency. Implement user-level rate limiting, moderation endpoints to filter unsafe content, and secure deployment practices.

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. This course maps to API Integration, OpenAI API, Cost Management for ML, Prompt Engineering, Word Embeddings.

API Integration
OpenAI API
Cost Management for ML
Prompt Engineering
Word Embeddings

Who it is for

Developers, AI builders. The material is pitched at expert level, and takes roughly 180 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 Tool: ChatGPT 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 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

All ChatGPT training courses →
ChatGPT: Advanced — Power Workflows & Automation — This course fills the step between everyday fluency and building on the platform: running ChatGPT at an expert level without writing code. Learners design multi-step workflows, automate recurring work with custom GPTs, scheduled tasks, and connectors, and set personal systems that compound over weeks. They finish as the person on the team others come to for ChatGPT technique.
ChatGPT: Core — Custom GPTs, Data Analysis & Reasoning — An advanced course covering ChatGPT's most powerful capabilities. Learners build custom GPTs, run data analysis with code interpreter, use reasoning models for harder problems, and connect external tools through actions. They finish able to tackle complex, high-value tasks.
ChatGPT: Essentials — Data & Analysis Playbook — A focused, 30-minute playbook for doing data and analysis work in ChatGPT. Learners run real tasks end to end - data cleanup, analysis, charts, and interpretation - and leave with repeatable workflows they can use the same day.
ChatGPT: Essentials — Research Playbook — A focused, 30-minute playbook for doing research and synthesis work in ChatGPT. Learners run real tasks end to end - sources, citations, deep dives, and verification - and leave with repeatable workflows they can use the same day.
ChatGPT: Essentials — Coding Playbook — A focused, 30-minute playbook for doing coding work in ChatGPT. Learners run real tasks end to end - code generation, explanation, debugging, and review - and leave with repeatable workflows they can use the same day.
ChatGPT Essentials Proficiency — Measures whether someone can choose the right model and mode, write a prompt that gets a usable first draft, and judge what comes back. Results land on the person's skill profile alongside the courses.
ChatGPT Mastery — Complete fluency in ChatGPT from first touch to building

Frequently asked questions

How long does this course take?

It runs roughly 180 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 TUL-GPT-301.