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1 Introduction to Generative AI for Software Testing
14 Lessons-
Preview1 Introduction to AI & Generative AI | Generative AI for Testers
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Start1.1 Generative AI Foundations and Key Concepts
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Start1.1.1 AI Spectrum Symbolic AI Classical MI Deep Learning GgenAI
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Start1.1.2 Basics of GenAI and LLMs
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StartModule 1 Quiz 1
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PreviewHands On | Calculate Tokens | Token IDs and Understand Embeddings
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Start1.1.3 Foundation Instruction Tuned and Reasoning LLMs
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Start1.1.4 Moltimodal LLMs and Vision Language Models
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StartHands On | Review and Execute a Given Prompt Addressing a Test Task
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Start1.2 Leveraging GenAI in Software Testing
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StartModule 1 Quiz 2
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Start1.2.1 Key LLM Capabilities for Test Tasks
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Start1.2.2 AI Chatbots and LLM Powered Test Applications
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StartModule 1 Quiz 3
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Download PPT Slides for Introduction to Generative AI for Software Testing
1 Lesson-
StartDownload PPT Slides for Introduction to Generative AI for Software Testing
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2 Prompt Engineering for Effective Software Testing
37 Lessons-
Start2 Prompt Engineering for Effective Software Testing
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Start2.1 Effective Prompt Development
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StartHands On | AI Prompting Basics in ChatGPT
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Start2.1.1 Structure of Prompts for GenAI in Software Testing
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StartHands On | Observe and Analyze Prompt Components
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StartHands On | Observing and Discussing Prompt Chaining, Few-Shot Prompting
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StartHands On | Identifying Prompt Engineering Techniques in Given Examples
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StartHands On | 6 Core Elements of a Good Prompt
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Start2.1.2 Core Prompting Techniques for Software Testing
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StartHands On | Top Prompting Techniques
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StartHands On | Zero-Shot Prompting
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StartHands On | One-Shot Prompting
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StartHands On | Few-Shot Prompting
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StartHands On | Chain-of-Thought Prompting
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Start2.1.3 System Prompt and User Prompt
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Start2.2 Applying Prompt Engineering Techniques Software Test Tasks
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Start2.2.1 Test Analysis with GenAI
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StartHands On | Practice Creating Structured Multimodal Prompts
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StartHands On | Practice Prompt Chaining and Human Verification
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Start2.2.2 Test Design and Implementation with GenAI
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StartHands On | Practice Functional Test Case Generation from User Stories
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StartHands On | Use the Few-Shot Prompting Technique to Generate Gherkin Style Test
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StartHands On | Use Prompt Chaining to Prioritize Test Case within a Given Test Suite
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Start2.2.3 Automated Regression Testing with GenAI
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Start2.2.3 Automated Regression Testing with GenAI (1)
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StartHands On | Few-Shot Prompting for Test Automation
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StartHands On | Practice Writing Structured Prompts for Test Report Analysis
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Start2.2.4 Test Monitoring and Test Control with GenAI
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StartHands On | Observe Test Monitoring Metrics Prepared by AI from Test Data
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Start2.2.5 Choosing Prompting Techniques for Software Testing
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StartHands On | Selecting Context-Appropriate Prompting Techniques for Given Test
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Start2.3 Evaluate GenAI Results and Refine Prompts
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Start2.3.1 Metrics for Evaluating the Results of GenAI
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StartHands On | Observe How Metrics can be Used for Evaluating the Result
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Start2.3.2 Techniques for Evaluating and Iteratively Refining Prompts
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StartHands On | Evaluate and Optimize a Prompt for a Given Test Task
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StartModule 2 Quiz 1
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Download PPT Slides for Prompt Engineering for Effective Software Testing
1 Lesson-
StartDownload PPT Slides for Prompt Engineering for Effective Software Testing
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3 Managing Risks of GenAI in Software Testing
21 Lessons-
Start3 Managing Risks of GenAI in Software Testing
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Start3.1 Hallucinations Reasoning Errors and Biases
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Start3.1.1 Hallucinations Reasoning Errors and Biases in GenAI
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Start3.1.2 Identify Hallucinations Reasoning Errors and Biases
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StartHands On | Experiment with GenAI Hallucinations Related to Software Test Task
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StartModule 3 Quiz 1
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Start3.1.3 Mitigation Techniques for Hallucinations Reasoning Errors and Biases
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Start3.1.4 Mitigation of Non-Deterministic Behavior of LLMs
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Start3.2 Data Privacy and Security Risks of GenAI in Software Testing
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Start3.2.1 Data Privacy and Security Risks Associated with using GenAI
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StartModule 3 Quiz 2
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Start3.2.2 Data Privacy and Vulnerabilities in GenAI for Test Process and Tools
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Start3.2.3 Mitigation Strategies to Protect Data Privacy and Enhance Security
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StartHands On | Data Privacy and Security Risks in Generative AI
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StartModule 3 Quiz 3
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Start3.3 Energy Consumption and ENV Impact of GenAI
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Start3.3.1 The Impact of using GenAI on Energy Consumption
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StartHands On | Use a Simulator to Calculate The Energy and CO₂ Emissions
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Start3.4 AI Regulations Standards and Best Practices
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Start3.4.1 AI Regulations Standards and Frameworks for GenAI in Software Testing
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StartModule 3 Quiz 4
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Download PPT Slides for Managing Risks of Generative AI in Software Testing
1 Lesson-
StartDownload PPT Slides for Managing Risks of Generative AI in Software Testing
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4 LLM-Powered Test Infrastructure for Software Testing
11 Lessons-
Start4 LLM-Powered Test Infrastructure for Software Testing
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StartHands On | Module 4
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Start4.1 Architectural Approaches for LLM Powered Test Infra
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Start4.1.1 Key Arch Components and Concepts of LLM Powered Test Infra
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Start4.1.2 Retrieval Augmented Generation
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StartModule 4 Quiz 1
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Start4.1.3 The Role of LLM Powered Agents in Automation Testing
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Start4.2 Fine Tuning and LLMops
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Preview4.2.1 Fine Tuning LLMs for Test Tasks
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Start4.2.2 LLOps When Deploying and Managing LLMs
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StartModule 4 Quiz 2
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Download PPT Slides for LLM-Powered Test Infrastructure for Software Testing
1 Lesson-
StartDownload PPT Slides for LLM-Powered Test Infrastructure for Software Testing
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5 Deploying and Integrating Generative AI in Test Organizations
14 Lessons-
Start5 Deploying and Integrating Generative AI in Test Organizations
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Start5.1 Roadmap for Adoption of GenAI
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Start5.1.1 Recall the Risks of Shadow AI
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Start5.1.2 Explain the Key Aspects to Consider When Defining GenAI
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StartModule 5 Quiz 1
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Start5.1.3 Key Criteria for Selecting LLM SLM
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StartHands On | Estimating the Recurring Costs of Using Generative AI
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Start5.1.4 Recall Key Phases in GenAI Adoption
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Start5.2 Manage Change when Adopting GenAI
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StartModule 5 Quiz 2
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Start5.2.1 Essential Skills and Knowledge for Testing with GenAI
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Start5.2.2 Building GenAI Capabilities in Test Teams
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Start5.2.3 Evolving Test Processes in AI Enabled Test Organizations
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StartModule 5 Quiz 3
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Download PPT Slides for Deploying and Integrating Generative AI in Test Org
1 Lesson-
StartDownload PPT Slides for Deploying and Integrating Generative AI in Test Org
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Download Full Course PDFs for ISTQB Generative AI (CT‑GenAI)
1 Lesson-
StartDownload Full Course PDFs for ISTQB Generative AI (CT‑GenAI)
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About This Course
Generative AI is reshaping software testing faster than most teams are ready for. The ISTQB CT GenAI certification is how you prove you understand it. This course covers the full CT GenAI syllabus in plain, practical language so you walk into the exam knowing exactly what to expect and walk out with the credential that sets you apart from other QA professionals.
Whether you are a fresher looking to get into software testing, already working in IT and considering a career change, a QA professional looking to upskill, or part of a corporate testing team seeking structured training aligned with ISTQB standards, this course is built for you. Every topic in the syllabus is explained with simple, real world examples rather than dry theory.
You will cover Generative AI foundations, how large language models work, prompt engineering techniques built for testers, test planning and design with AI, how to apply Generative AI to test execution and analysis, risk management and bias mitigation, and responsible and compliant use of AI in QA. The course also includes practice questions and mock tests so you are exam ready, not just course ready.
By the end you will have the knowledge, confidence, and certification credentials to stand out in the job market and accelerate your career in AI driven testing. This course not only prepares you for the exam, it equips you with practical insights into how Generative AI is changing quality assurance right now, across real enterprise projects.
What You'll Learn
Generative AI Foundations
Understand what Generative AI is, how large language models work, and what makes them fundamentally different from traditional software. Learn how AI generates outputs and why that matters for software testing.
Prompt Engineering for Testers
Write prompts that work for QA tasks. Learn prompt design techniques, how to get consistent outputs from LLMs, and how to evaluate and refine prompts for test generation and analysis scenarios.
Test Planning and Design with AI
Discover how Generative AI can support test strategy, test case generation, and coverage planning. Learn where AI adds the most value in the test design lifecycle and where human judgment still leads.
Test Execution and Result Analysis
Apply Generative AI to test execution support, log analysis, and failure triage. Understand how AI tools can help teams process results faster and surface insights that would take hours to find manually.
Risk Management and Bias Mitigation
Identify the risks unique to AI powered systems, including hallucination, bias, and unpredictability. Learn structured approaches to assess, monitor, and mitigate AI risks in a testing context.
Responsible and Compliant AI Use
Understand ethical considerations, compliance obligations, and responsible AI governance as they apply to software testing teams. Learn what the ISTQB syllabus expects you to know about safe and compliant AI adoption.
CT GenAI Exam Strategy and Practice
Work through practice questions and mock tests aligned to the official CT GenAI exam format. Understand how questions are worded, what the examiners are looking for, and how to approach tricky scenario based questions with confidence.
AI Tools in the QA Workflow
See how Generative AI tools are being integrated into real QA workflows today. Understand the landscape of AI powered testing tools, their strengths and limitations, and how to evaluate them for your own team or project.
Meet Your Instructor
Manish Verma
Founder & Senior QA Architect | ISTQB Certified AI Testing Expert
20+ years of QA and project management experience, holding multiple ISTQB certifications including AI Testing and Testing with Generative AI, with deep practical expertise in applying Generative AI concepts to real software testing environments and preparing QA professionals for ISTQB certification exams
Manish is the founder of RCV Academy and a recognized leader in software testing and QA education. With extensive experience delivering QA programs across IBM, Capgemini, and multiple enterprise environments, he brings practical testing knowledge grounded in real projects, not just textbook theory.
His teaching approach is practical and outcome focused. He simplifies complex topics like agentic AI, multi agent orchestration, and LLM based test automation into clear, actionable lessons that testers at every level can apply immediately. Over 225,000 students trust his content across multiple platforms, making him one of the most recognized QA and AI educators in the industry.
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What You Need
Course Requirements
- Anyone can take this course. No prior experience is needed.
- To gain the ISTQB CT GenAI certification, you must hold the Certified Tester Foundation Level (CTFL) certificate.
Frequently Asked Questions
Do I need any prior experience in AI or testing to take this course?
No. Anyone can take this course regardless of their background. The content is designed to be accessible to absolute beginners. If you already have experience in software testing, you will move through the early sections quickly and find the AI specific content especially useful. The only requirement for the actual ISTQB certification is the CTFL foundation level certificate.
What is the ISTQB CT GenAI certification and why does it matter?
CT GenAI stands for Certified Tester Generative AI. It is an ISTQB specialist certification that validates your understanding of how Generative AI applies to software testing. As AI tools become standard in QA teams, this certification is becoming a strong differentiator in the job market for testers, leads, and QA managers.
Does this course cover the full CT GenAI syllabus?
Yes. This course is built around the official ISTQB CT GenAI syllabus and covers every topic area you will be tested on. You will also get practice questions and mock tests formatted to match the real exam so you know what to expect on the day.
I am not a tester. Can I still benefit from this course?
Absolutely. Business analysts, project managers, operations team members, IT directors, and management consultants who work alongside QA teams will find this course valuable. Understanding how Generative AI affects software quality and testing decisions is relevant to anyone who is part of a product or delivery team.
How is this course different from just reading the ISTQB syllabus?
The syllabus tells you what to know. This course explains it in plain, practical language with real examples from actual testing scenarios. You will understand the reasoning behind each topic, not just memorize definitions. That depth is what helps you answer scenario based exam questions confidently and apply the concepts at work.
How much time do I need to complete this course?
The course has 9.5 hours of content. Most students complete it over one to two weeks studying a few hours each day. If you are preparing for the exam, we recommend going through the practice questions and mock tests after finishing the content sections before booking your exam date.
Who This Course Is For
Software Testers, QA Engineers and SDETs
Whether you are new to QA or working in automation, this course gives you the ISTQB recognized credential that validates your understanding of Generative AI in testing contexts. It is the natural next certification after CTFL.
QA Leads, Test Leads and Managers
If you oversee QA teams and want to understand how to responsibly adopt AI tools, manage AI related risks, and guide your team through AI driven change, this certification prepares you to lead with confidence and credibility.
Business Analysts, Software Dev Managers and Project Managers
Understanding how AI affects software quality and delivery is increasingly important for anyone who shapes product decisions or manages delivery teams. This course gives you the vocabulary, frameworks, and knowledge to engage meaningfully with AI powered QA initiatives.
Operations Team Members
Operations professionals who work with software systems need to understand how AI introduces new categories of risk and unpredictability. This course covers the reliability, compliance, and governance dimensions of Generative AI that are directly relevant to ops roles.
IT Directors
Technology leaders who are evaluating or approving AI adoption across their organizations will benefit from understanding the testing, risk, and compliance implications that this certification covers. It gives you the informed perspective needed to make sound governance decisions.
Management Consultants
Consultants advising clients on digital transformation, AI adoption, or software quality will find this certification adds credibility and structured knowledge. It positions you to speak authoritatively about the quality and testing dimensions of AI projects.
30 Day Money Back Guarantee
Your satisfaction is guaranteed. Enroll risk free. If the course is not what you expected within 30 days, get a full refund with no questions asked. We are this confident you will love it.
