Accelerated Software Engineering Chat GPT and AI Agents

Name: Accelerated Software Engineering Chat GPT and AI Agents
Rating: 4.5 (3185 reviews)

Understand how Software Engineers can leverage Generative AI with practical examples and some futuristic use cases

Created byDan Andrei Bucureanu

Last updated 9/2025

English

German [Auto],English [Auto],

What you'll learn

Main Features of Google GEMINI AI
Main Features of OPENAI CHAT GPT
Setup CI/CD pipelines with AI
Generative AI for Quality Engineering
Create a full performance test framework with Jmeter in the cloud with CHATGPT
Create a full API Testing test framework with Jmeter and RESTAssured in the cloud with Google BARD
Setup test tools with Google BARD
Improve efficiency of working with Google BARD
Connect to CHAT GPT API
Train your own instance of GPT
Gen AI example 1 - Story Enhancement
Gen AI example 2 - Self Healing Code
Gen AI example 3 - Production Recursive Testing
Gen AI example 4 - Self Service Bot
Gen AI example 5 - Recommendation Engine for E-commerce
Example of Different models and usage based on ML type
Understand Machine learning
Improve Test automation with ChatGPT and Google BARD
How to use ChatGPT and Google BARD for Quality Engineering

Course content

20 sections • 159 lectures • 11h 24m total length

Introduction7:43
Examine generative AI architectures and the stack for integrating AI into apps. Learn to configure Python, Node.js, VSCode, and Cursor AI, build GPT-based helpers, and orchestrate agents for practical experimentation.
About your Instructor2:00
With 20 years in quality engineering, the instructor has led teams from 2 to 80, tested code for startups and banks, and shares insights across agile, cloud, and machine learning.
Do not skip - AI for Testing the DOs and DONTs21:39
Understand what are the limitation, what you can and cannot do in all phases of a software testing project
Prompt Anatomy4:17
How does the App Tech Stack Look with AI4:04
Explore how adding AI changes a traditional decoupled app architecture, introducing an AI layer with model APIs, vector databases, and orchestration to drive inference and agents.

Install Python2:18
Install python on windows via the microsoft store, which resolves dependencies automatically. Verify the install with python --version in the terminal and prepare the remaining dependencies for benchmarking.
Install PIP for Python2:07
Install NPM and Node.js2:47
Install node.js and npm by downloading the prebuilt installer from node.org, verify with node -v and npm -v, and use npm install to resolve dependencies.
Install VSCode2:40

What makes up AI3:47
Get a brief introduction on what are the main components of AI
Introduction to Natural Language Processing NLP4:56
How NLP actually makes the AI more human.
Types of Machine Learning4:08
Understand what is machine learning and how algorithms make the core of AI
Machine Learning - Supervised ML4:01
Understand the basics concepts around supervised Machine Learning
Machine Learning - Unsupervised ML5:03
Gain basic understanding of Unsupervised ML and Clustering
Machine Learning - Reinforced ML3:20
In this lecture you will get a basic idea of how Reinforced Learning is working together with ML Algorithms
Neural Networks and Deep Learning5:26
In this material you will understand how critical good quality training data actually is.
Importance of Training Data4:09
In this material you will understand how critical good quality training data actually is.
Transformer Architecture Model0:03

Productivity Hack with Chat GPT and Mobile app6:30
Link to android app -> https://play.google.com/store/apps/details?id=com.openai.chatgpt&hl=en&gl=US
Link to apple app -> https://apps.apple.com/us/app/chatgpt/id6448311069
A word of introduction on AI Use Cases1:14
AI Use Case 1 - Artifact Enrichment - How it works5:24
Learn how to build a self-enriching item workflow that uses prompts and foundation models to iteratively enrich, robustify, and finalize outputs, with reinforced learning for future improvements.
AI Use Case 1 - 2 Architectures for Gen AI Enrichment5:58
Explore two architectures for gen AI enrichment: a traditional manual prompting workflow and a largely automated custom-application approach with cascade prompts and API integration.
AI Use Case 1 - [Demo Generative AI]- Enrich Content3:16
AI Use Case 1 - [Demo Generative AI] Enrich Content - Using API Client1:53
Explore enriching content with an API client for a custom model, automating prompts, removing noise, and delivering focused results via post requests and token-aware workflows.
AI Use Case 2 - Recommendation Engine with AI - How it Works4:05
Explore a non-generative ai-based e-commerce recommendation engine built on artificial neural intelligence. Feed customer demographics and cart content into an unsupervised model to suggest items and refine with delta feedback.
AI Use Case 2 - [Demo ]- AI Assisted Shopping Recommendation Engine4:03
AI Use Case 3 - Self Healing Code2:14
AI Use Case 3- [Demo Generative AI] Self Healing Code - GItHub3:55
Explore how to use generative AI to self-heal code within a GitHub workflow, automatically fix unit tests, improve code coverage, and retrigger continuous integration with human oversight.
AI Use Case 4- Tests based on Analytics - Preconditions4:57
Adopt analytics-driven, behavior-based testing for mass-market SaaS using BDD with Gherkin and NLP, mapping human-readable scenarios to given-when-then tests and user journeys.
AI Use Case 4- Tests based on Analytics - How it Works4:56
Learn how a next-generation testing framework uses analytics-driven given-when-then acceptance tests, continuous in production monitoring, and trained models to prioritize scenarios and reveal new ones from user behavior.
AI Use Case 5 - Self Service Portal Chatbot4:35
Explore how generative ai and large language models power a self-service portal chatbot, bridging users to back-end it systems through apis and a trained model.
AI Use Case 5 - Demo Trained Model - Self Service Bot4:20
See how a trained model powering a self-service bot reads the washing machine user manual to answer troubleshooting questions and information on main programs and temperature by scanning the document.

What is a Token in LLM2:38
Learn how tokens define text units in large language models, how text is decomposed into tokens, and how token counts affect prompts and api usage using tokenizer examples.
Chat GPT-API - Create Subscription1:33
Chat GPT-API - Get API Key1:00
Generate a new secret key for your account in the API keys panel. Use it for demos and any get or post requests, and never share it.
Chat GPT-API - Completion API Call5:55
Discover how to perform your first completion API call with curl, using the GPT-3.5 model, API key, and temperature settings while navigating tokenization and simple quickstart guidance.
Chat GPT-API - DALL-E API Call2:41
Learn to call the DALL-E 3 image API with curl, supply an API key and prompt, set image count and size, and receive image URLs with Python and Node.js samples.

What are AI Agents5:22
Explore AI agents as orchestrators coordinating prompts and large language models to solve tasks. Learn how they use workflow routing, prompt chaining, and feedback loops across APIs, databases, and code.
DEMO - Agents that work on user stories4:26
Watch AI agents generate, review, and refine a user story with acceptance criteria and readiness checks, then publish to Jira using an MCP protocol.
Demo - Using AI Agents to Identify Test Cases3:55
Demonstrate how AI agents define test cases from acceptance criteria, apply test design best practices, generate Jira-posted tests, and enable automated testing of a bank customer story.
AI Agents will perform acceptance test and log steps4:02
Automate acceptance testing for an AI agent by simulating a flight booking from Boston to New York, using browser use as a ChatGPT operator and logging all steps and data.
Demo - Copilot Playwright Script Generation4:03
Demonstrates Copilot-driven Playwright script generation to create automated tests from prior browser interactions, using the page object model and decoupled test data for new scenarios.
AI Agents Automatic Peer Review - PR Close Review5:33

Explain a concept with CHATGPT6:09
Explore shift left and shift right concepts in software development and computer science, illustrated through prompts, memory updates, and practical tips on test driven and behavior driven development.
Rapid Start your documentation10:14
Use ChatGPT to generate a high level ERP test strategy, with prompts at basic to advanced levels, canvas, and covering functional, integration, and performance testing with data integrity.
Use CHATGPT to help in creating an robust test framework8:53
Use ChatGPT to validate and enhance User Stories4:57
ChatGPT is used to create and validate user stories for an ATM withdrawal. The lesson adds acceptance criteria and non-functional requirements such as data privacy and fraud prevention.
Using ChatGPT for generating Test Cases from Acceptance Criteria9:55
Generate user acceptance test cases from a story using ChatGPT, a template, and given–when–then scenarios to validate functional requirements.
Understand how to use Chat History to keep context4:26

Open AI Codex - Understand what is Codex - 01 Agent3:41
Explore OpenAI Codex, a coding agent powered by Codex one from the O3 model with human feedback; connect to GitHub, code or ask, run, and configure with plus subscription.
Open AI Codex - Configure Codex for your environment7:11
Open AI Codex - Configure the Agents.md File5:50
Open AI Codex - Create a powerful Agent.md file - fix the previous one7:00
Craft a proper agents.md with clear instructions, config, pages, tests, and usage notes, and ensure no tests run unless explicitly instructed.
Open AI Codex - Use Codex to generate invalid test data6:51
Explore using OpenAI Codex to generate invalid and valid test data, expand data sets, and manage environment tasks and pull requests.
Open AI Codex - Generate Test that use invalid test data to simulate failure7:59
OpenAI Codex helps generate negative invalid test data to simulate failures, manage json test data, and review changes via the master branch and pull requests.

Agentic AI | AI Agents - What are AI Agents5:22
Explore what artificial intelligence agents are, and how orchestrators coordinate prompts, large language models, tools, databases, and workflows to automate complex tasks in software development.
Agentic AI - Demo On AI Agent that can Automatically Create Tests6:43
Agentic AI | AI Agents - What is Flowise AI3:44
Agentic AI | AI Agents - Installing Flowise AI Locally5:05
Agentic AI | AI Agents - Prompt Chaining9:42
Build an ai agent through prompt chaining, using a supervisor orchestrator and worker nodes connected to a ChatGPT wrapper, configured with an OpenAI API key and model.
Agentic AI | AI Agents - Interact with files6:58
Orchestrate two AI agents to read a story from a file, generate test cases and BDD scenarios, and write the results to disk using a read/write tool and prompt engineering.
Agentic AI | AI Agents - Generate Automated Tests from Webpage12:23

What is Microsoft Autogen - Agentic Creation Framework3:22
Explore Microsoft Autogen, an open source framework for a generic artificial intelligence, featuring a no-code studio and multi-agent orchestration to build apps with Python or .NET.
Installing Microsoft Autogen4:50
Install Microsoft Autogen following official instructions in an isolated VM, create and activate a dedicated environment, install Autogen and OpenAI dependencies, and test a simple chatbot.
Autogen - How to define an agent -Chatbot5:53
Define a chatbot agent with Autogen, supply a GPT API key from the environment, and explore built-in memory as you chat.
Autogen - Create 2 agents that talk to each other5:46
Learn how to autogenerate two talking agents that critique and refine a Python function, debating to reach a robust solution with unit tests, using autogen memory for memory handling.
Autogen - Adding success criteria - End Conversation3:58

Requirements

no programming experience needed
curiosity to learn the hottest topics available now
basic experience with software engineering

Description

This introductory course is designed specifically for Software Quality Engineers interested in leveraging the power of Generative AI to enhance their testing, automation, and productivity.

Throughout the course, participants will learn how to apply Generative AI techniques to automate the generation of test cases, simulate user behavior, create user stories, write better code, rapid start adoption of a new framework and generate test data. The course will provide a practical understanding of how AI can be used to improve software quality and boost productivity, rather than focusing on the underlying AI algorithms and models.

Topics:

Understanding Generative AI: An overview of Generative AI and its applications in software testing and quality assurance.
Automated Test Generation: Learn how to use Generative AI to automatically generate a variety of test cases, reducing manual effort and increasing test coverage.
API Access for Chat GPT and Google Bard: Understand how to call Bard and GPT with the help of customer made consumers
Automated User Story Creation: Understand how Generative AI can be used to generate user stories, helping to streamline the requirements gathering and software development process.
Test Data Creation: Learn how Generative AI can be used to generate test data, ensuring comprehensive and effective testing while saving time and effort.
Code Investigation: Discover how Generative AI can assist in investigating complex code, identifying potential issues, and understanding code dependencies.
Code Explanation: Learn how Generative AI can be used to explain code behavior, making it easier to understand complex code and improve code quality.
Boosting Productivity: Understand how these Generative AI techniques can be integrated into your existing workflows to boost productivity, improve software quality, and accelerate delivery times.
[Practical] See how to create a Performance testing Framework with CI/CD on cloud with AI
[Practical] Create an API Testing Framework with Java and RESTAssured with AI
[Practical] Code Quality Validation Framework for Java
See differences between CHATGPT and GOOGLE BARD
Google Cloud AI solution with model training - Vertex AI
AI-Powered Browser Automation – Master sophisticated AI operators for seamless, intelligent browser automation.
Self-Learning AI Agents – Develop adaptive AI agents capable of self-improvement, continuous evaluation, and autonomous decision-making.
Make agents communicate and solve issues with MSFT Autogen. An agent collaboration framework

Who this course is for:

Business Analysts
Quality Engineers
Software Engineers
Scrum Masters
Engineering Managers

Accelerated Software Engineering Chat GPT and AI Agents

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 40min

Prepare Developer Environment4 lectures • 10min

Introduction to Generative AI9 lectures • 35min

A.I Use Cases in Product Development and Engineering14 lectures • 57min

Connect to CHAT GPT with a Client - API5 lectures • 14min

AI Agents example how to use them across Testing Lifecycle6 lectures • 27min

CHAT GPT - Accelerate Test Artifact Generation6 lectures • 45min

Open AI Codex - AI Coding Agent for Testing6 lectures • 39min

AI AGENT - Create your OWN AI Agent with FlowiseAI7 lectures • 50min

Microsoft AutoGen - Collaborating Agents5 lectures • 24min

Requirements

Description

Who this course is for: