ChatGPT Complete Guide: OpenAI API, AI Tools, ChatGPT 4

Name: ChatGPT Complete Guide: OpenAI API, AI Tools, ChatGPT 4
Rating: 4.5 (143 reviews)

Learn ChatGPT from Scratch, best practices, build solutions & apps with AI, we create app for Jira, Slack, Confluence

Highest Rated

Created byAndrii Piatakha

Last updated 2/2026

English

What you'll learn

OpenAI API
ChatGPT
Best Practices of using ChatGPT
Slack Messenger API
Jira Software Cloud API
ChatGPT, Slack, Jira Integration via Java Spring Boot Web App
Create Web App via Spring Boot, ChatGPT, Jira, Slack for Project Management Operations Automation
Project Management with ChatGPT
Project Governance with ChatGPT
Knowledge Base Management with ChatGPT
Risk Management with ChatGPT
Planning and Estimation with ChatGPT
Requirements creation and requirements management with ChatGPT

Course content

20 sections • 87 lectures • 27h 37m total length

Communication plan4:10
From this lecture you will learn:
• How to communicate during this course
• Where to ask questions
• How to ask questions
• Communication channels
Tips to Improve Your Course Taking Experience0:44
Learn IT Bot – Your Free AI Learning Assistant17:50
Why I Created This Bot
The Challenge: From Passive Learning to Real Mastery
The Solution – Learn IT Bot
Inside the Learn IT Bot – Key Features
Adaptive Difficulty & Endless Practice
Live Demo of the Learn IT AI Bot
Why It Matters – From Learning to Real-World Readiness
Free AI Bot for My Students Only – No Sign-Up, FREE, Just Practice1:50
In this lesson, I’ll show you how my students get exclusive, free, no sign-up access to a one-of-a-kind AI Bot I personally built to help you deeply learn the material, reinforce your knowledge, and gain a real advantage in interviews, real-world work and career growth.

Intro to the OpenAI Section0:19
OpenAI: Basic Concepts27:25
What is OpenAI API
What is ChatGPT
OpenAI VS ChatGPT
Key Terms and Concepts in OpenAI API
Prompt
Tokens
Models
Review of key models
GPT-4 Model
GPT-3.5 Model
DALL·E Model
Whisper Model
Embeddings
Moderation Model
GPT-3 Model
Point·E Model
Jukebox Model
CLIP Model
Codex Model
OpenAI API: Create & Manage Personal Account27:54
Account creation at OpenAI
API Reference & Documentation
Playground Overview
Manage account settings
Usage Limits
Pricing
Understanding the importance of a "context"
Configuration of Billing
Soft & Hard billing limits
Rate limits
RPM & TPM
How to invite members into your organization
Creation of secret API key
What is model fine-tuning
Service-status

Important note about API Testing0:18
ChatGPT: Chat API30:40
Chat VS Completions API
When to use Chat API
When to use Completions API
Overview of Chat API
Model Endpoint Compatibility
Roles: System, User, Assistant, Function
What “temperature” to use
Detailed review of Chat API attributes
model attribute
messages attribute (incl. role, name, content, function_call)
Temperature attribute
top_p attribute
n attribute
stream attribute
max_tokens attribute
presnece_penalty attribute
frequency_penalty attribute
logit_bias attribute
user attribute
Send First Request to GPT API & Parse Response25:09
Authentication & Authorization in OpenAI API
Selecting model for request
Send request to OpenAI API GPT model from Postman
Deprecations
Parsing GPT response
“id” attribute
“object” attribute
“created” attribute
“model” attribute
“usage” attribute
“choices” attribute
“message” attribute
“finish_reason” attribute
First Web Application with ChatGPT24:23
Select programming language
Overview of official libraries for OpenAI API
Community libraries for OpenAI API
Create the first Web Application for ChatGPT Integration
Review of the Application’s Architecture
Function calling in ChatGPT33:35
What is function calling
Business need/Review of use cases
Function calling algorithm
OpenAI Chat API review
function_call: “auto”
function_call: “prompt”
function_call: “name”
function_call: “none”
Request for a function call in GPT Response
Code examples review
Live demo
GPT Best Practices34:16
Strategy: Write clear instructions
Tactic: Include details in your query to get more relevant answers
Tactic: Ask the model to adopt a persona
Tactic: Use delimiters to clearly indicate distinct parts of the input
Tactic: Specify the steps required to complete a task
Tactic: Provide examples
Tactic: Specify the desired length of the output
Strategy: Provide reference text
Tactic: Instruct the model to answer using a reference text
Tactic: Instruct the model to answer with citations from a reference text
Strategy: Split complex tasks into simpler subtasks
Tactic: Use intent classification to identify the most relevant instructions for a user query
Tactic: For dialogue applications that require very long conversations, summarize or filter previous dialogue
Tactic: Summarize long documents piecewise and construct a full summary recursively
Strategy: Give GPT time to "think"
Tactic: Instruct the model to work out its own solution before rushing to a conclusion
Tactic: Use inner monologue or a sequence of queries to hide the model's reasoning process
Tactic: Ask the model if it missed anything on previous passes
Strategy: Use external tools
Tactic: Use embeddings-based search to implement efficient knowledge retrieval
Tactic: Use code execution to perform more accurate calculations or call external APIs
Tactic: Give the model access to specific functions
Strategy: Test changes systematically
Tactic: Evaluate model outputs with reference to gold-standard answers

Create Slack Application, Configuration of Incoming & Outgoing Webhooks24:58
What is slack
Slack Installation
Create an account in Slack
Create a workspace in Slack
Create a channel in Slack
Create an Application in Slack
Creation of app from scratch
Creation of app from an app manifest
Incoming and Outgoing Webhooks
Events API in Slack
Configure outgoing and incoming webhooks in Slack
Connect GPT to the Slack27:20
Architecture overview
Review of code examples
How to add the app to a channel
Slack event payload review
Slack Java SDK
Overview of SDK for other programming languages
Scopes in Slack
Configuration of required scopes for the app
Set up app picture
Context Management in Web App to Integrate with GPT22:32
How to manage the context during the integration with GPT
Code examples review
How to limit the context length
How to manage the context of the Slack Team
Remove all messages from a channel

The Role of Jira in our Web Application | Why this section is needed?0:17
First Project in Jira Cloud24:41
What is Jira
Jira Editions & Deployment Options
Advantages of Jira Cloud
Jira analogs & competitors
Create an account in Atlassian
Create Jira Project
Kanban Template VS Scrum Template
Jira Kanban Board overview
Jira project settings overview
How to add issue types to the project
How to add fields to issue type
Create fake data in the project
Jira API21:19
Jira API Review
Jira API Versions
Authentication & Authorization in Jira API
What is Forge App in Atlassian
What is Connect App in Atlassian
JWT
3LO
Create API Token for Atlassian Account
Encrypt credentials in Base 64 encoding
Jira API calls via Postman
Jira API calls from web app
Jira API documentation overview

GPT + Slack + Jira Integration: Work with Jira Datasource36:19
How to integrate Jira API with GPT, Slack and Web Application
Connect Jira as a separate datasource
Read the required context from the Jira
Function calling implementation to fetch required Jira items
Demo of real-life application
Web Application architecture overview
Best practices
Generate Tickets in Jira & Send Email from Slack via Chat Interface22:28
Generate work item description with the help of ChatGPT
Create work item in Jira using chat interface
Assign team member for Jira ticket directly from slack
Set the due date for Jira work item from Slack
Generate an email
Send an email using email address using chat interface
Send an email using the name of the person using chat interface

Managing Scrum & Risk Management with Custom Bot, Slack & GPT11:43
In the video I’m going to hold a demo of few more use cases of how you can use our bot developed during the course together with ChatGPT to manage Scrum Team, calculate average velocity and plan a new Sprint. Also in the video I’m going to show you Jira board configured for execution of Risk management and how I use my bot to manage risks on my projects.
In the lesson I used the approach with function callback that we learned in previous lessons.
You can find source code in attachments to the video.
In case you will have any questions, please, post your questions below the video and I will be happy to answer.
During the video we will review:
How to calculate average velocity with ChatGPT
How to plan Sprint based on priorities and team’s capacity
How to check status of risks
How to work with not processed risks
How to execute risk management operations using chat interface

Fine-tuning: Key Concepts & Overview of the Process30:51
What is fine-tuning
Fine-tuning steps/algorithm
Labeled data
Few-shot learning
Meta-learning or learning to learn
Model-Agnostic Meta-Learning (MAML)
Reptile
Prototypical Networks
Matching Networks
Memory-Augmented Neural Networks
Transfer learning
Difference between meta-learning & transfer learning
Advantages & benefits of fine-tuning
Use cases and examples when we need fine-tuning
Available models for fine-tuning
Chat API VS Completions API
Ada, Babbage, Curie, Davinci models overview
Fine-tuning costs
Important note about updates in GPT fine-tuning2:00
In this lesson, I’m going to share with you important notes about the most recent updates before you start watching the next lessons about fine-tuning.
Preparing Training Dataset: Key Principles, Use cases & Examples - PART 119:24
Training dataset format
What is JSONL
How much examples we need for training dataset
Data Augmentation
Overfitting
Availability & Feasibility of Data
Preparing Training Dataset: Key Principles, Use cases & Examples - PART 217:59
Classification use cases and examples
Conditional generation use cases and examples
Guidelines for preparing training dataset for classification use cases
Guidelines for preparing training dataset for conditional generation use cases
What is learning rate
What is epoch in machine learning
Prepare Training Dataset
Create your first fine-tuned model: Part 127:06
Requirements for our first custom chat bot
Prepare training dataset using ChatGPT
Prepare dataset for validation
OpenAI Python Client
Install Python
What is PIP
Install OpenAI Python Client
Prepare datasets for training
Create your first fine-tuned model: Part 226:54
Fine-tune the model
What is the batch size
What is the loss weight
Demo of using of fine-tuned model
Analysis of fin-tuned model
How to analyze training process
=== EXAM: Create and Integrate Custom ChatBot for Confluence into the Slack ===0:03
Chat Bot for Knowledge Base and How to build iterative process of fine-tuning12:54
Practical Use Case Demo: Chat Bot for Knowledge Base
Integration of custom chat bot via Slack Messenger
How to build iterative process of fine-tuning

Requirements

For programming part of the course and for Web App development I use Spring Boot and Java language. You can create web app using your own tech stack, but understanding of Java will help you to understand the key concepts I want to teach

Description

In this course, we are going to Learn ChatGPT in Depth.

Advantages of this course:

Huge amount of source code examples: Even the first edition of this course already contains around 1000 files that can be used as examples. And this is just for one project that we develop with students. Not talking about examples that I share on the slides, or during the no-code development. This course is extremely oriented on practice and business use cases. And new examples are added to the course on a regular basis, because I update this course with new use cases, with new updates after new releases of OpenAI ChatGPT model. We are going to develop web application to manage project management operations using OpenAI API.
Concentration of useful materials: Cut to the chase - No water. In this course you will not find 10 hours of lessons teaching you how to enter text in the chat GPT web application. We are going to learn a lot of things, and what is the most important, we are going to learn a lot of different things.
Vast experience in the subject: my company was one of the first on the market that started consulting clients about ChatGPT since GPT API was publicly exposed.
Q&A Support and Close collaboration during the course: at the end of the day, you don't just get the video lessons, you also get support from me. We work in close collaboration, you ask your questions about topics discussed in the video, source code reviewed and other things. No matter what questions you have, I'm here to help.
Professional learning approach: I'm a tutor with 900 students from more than 200 countries around the world. I was an offline tutor for a long time, and then I founded Learn IT Online University. I have a lot of experience in communication and teaching students both: offline and online. And I can easily find the right approach to explain things, and make complex things easier to understand.
No drama money-back guarantee: In case you didn't like the course, for any reason, you should explain nothing. You can easily get your money back within the 30 days after registration. I promise you. So, there is no risk at all for you. In case you don't like the course, you can quit anytime you want.

Target Audience of the Course:

This course is designed for everyone who wants to learn OpenAI API. I can say that this is the most detailed and most complete OpenAI API course available online based on today
A significant part of the course will be dedicated to learning the OpenAI API. During the course we are going to create our own web application, and develop chat bot - that's why this course will be interesting for developers
But even despite the fact that I have lessons with coding examples, I still believe that this course will be interesting for Product Managers, Product Owners, and Project Managers. I know this because very often I receive requests from Product Managers asking me about capabilities of ChatGPT, business use cases, technical limitations, and similar questions. This course will help you to get a deep understanding of how ChatGPT works under the hood, and what its weak and strong sides are that you can take advantage of.

Who this course is for:

Everyone who wants to learn ChatGPT in depth
Web Developers who want to create web applications using ChatGPT capabilities
Product Managers who wants to explore and learn technical details and ChatGPT limitations to udnerstand how to build products using ChatGPT

ChatGPT Complete Guide: OpenAI API, AI Tools, ChatGPT 4

What you'll learn

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Course content

Introduction4 lectures • 25min

ChatGPT Web Application1 lecture • 23min

OpenAI3 lectures • 56min

OpenAI API: ChatGPT Model6 lectures • 2hr 28min

Web Hosting at Home1 lecture • 17min

GPT + Slack Integration3 lectures • 1hr 15min

Jira3 lectures • 46min

GPT + Slack + Jira + Gmail Integration2 lectures • 59min

Manage a Scrum Team with ChatGPT1 lecture • 12min

Fine-tuning, Creation & Training of Custom GPT Model8 lectures • 2hr 17min

Requirements

Description

Who this course is for: