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Highly Rated

GenAI for QA Engineers

Generative AI

AWS Certifiation Course
AWS Certifiation Course

Unlock the power of Generative AI in QA

Generative AI is revolutionizing Quality Assurance (QA) by automating complex testing processes. With cutting-edge tools like GPT-4, LlaMa, DeepSeek and advanced Large Language Models (LLMs), QA teams can automate test case generation, streamline defect reporting, and rapidly generate precise test data and scripts. This powerful technology reduces manual effort, enhances accuracy, and accelerates software testing cycles significantly.


This 8-week comprehensive course is specifically designed to empower QA engineers by integrating Generative AI into everyday QA workflows, preparing you to become a top-tier professional in the rapidly evolving field of software testing.


AWS Certifiation Course

Upcoming Classes

Transform your testing expertise by mastering Generative AI through hands-on learning and guided projects. Achieve your career goals with expert mentorship.


19 Apr 2025

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CLOSING TIME

Generative AI in QA Features

Deep AI Knowledge

Gain a comprehensive understanding of AI, ML, DL, and LLM fundamentals to effectively integrate AI into your QA workflows.

Practical Use Cases

Explore over 10 real-world use cases of Generative AI solutions specifically tailored for QA and test automation.

Custom Tool Building

Learn to build custom QA tools using powerful Large Language Models (LLMs), enabling tailored test automation solutions.

Streamlined Test Automation

Master the seamless generation of Selenium, Playwright, and RestAssured scripts directly from DOM and Swagger files.

Automated Test Data and Reporting

Effortlessly generate realistic test data and automate defect reporting to optimize your testing cycles and improve efficiency.

Career Transition Roadmap

Obtain a structured roadmap to smoothly transition from manual or traditional automation roles to advanced Generative AI-driven QA positions.

Skyrocket your career growth
by Upgrading Your Skills

Enroll Now

    The #1 GenAI Training Program; Here’s Why?

  • Selenium Training in Chennai 4.9 Star Rating
  • Selenium Training in Chennai 2,825 Google Reviews
AWS Certifiation Course

How important is GenAI for QA Engineers Training?


Generative AI (GenAI) is rapidly reshaping the software testing industry, making specialized training crucial for QA professionals. This course provides the critical skills and knowledge needed to effectively integrate GenAI and Large Language Models (LLMs) into QA practices, ensuring increased productivity and enhanced career opportunities.

The training covers foundational concepts through advanced applications, emphasizing hands-on exercises, real-world use cases, and project-based learning. Participants will:


  • ➤ Master automated test case generation and optimized test coverage
  • ➤ Develop skills in prompt engineering and automation scripting without extensive coding experience
  • ➤ Efficiently manage defect triaging, log analysis, and test documentation


QA engineers skilled in Generative AI stand out for roles such as AI Test Engineer and Test Automation Specialist, making them highly valuable as more companies adopt AI-driven testing solutions.

Short Bio about GenAI Trainer:

Babu Manickam

GenAI Early Adopter

He is an expert in Generative AI. Passionate about cutting-edge AI technologies, he actively researches and develops innovative solutions in the realm of machine learning, natural language processing, and AI-driven Test Automation.

  • GenAI Expert/ Trainer
  • 25 Years of Experience

Best way to learn GenAI for QA Engineers

GenAI for QA Engineers is specifically designed to transform your QA skill set through practical, hands-on experience with advanced Generative AI techniques. The course covers foundational to advanced-level concepts, including AI, ML, DL, and Large Language Models (LLMs) such as GPT-4, Llama, and DeepSeek. You’ll explore real-world scenarios, such as automating test scripts for Selenium and Playwright, converting Swagger YAML into RestAssured scripts, generating realistic test data, and automating defect reporting. The program guides you smoothly from traditional manual or automation testing to advanced, AI-driven QA roles. Master cutting-edge GenAI tools and confidently integrate them into your QA processes, making you competitive and future-ready.


Launching Date: 19 Apr 2025 Enroll Now
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GenAI for QA Course Content

Understanding AI and Generative Models
    Basics of Artificial Intelligence and Machine Learning
    - Key concepts in AI and ML
    - Difference between AI, ML, and Deep Learning
    Introduction to Generative AI
    - What is Generative AI?
    - Evolution and applications
    Large Language Models (LLMs): An Overview
    - Understanding LLMs
    - Impact on various industries and domains
    Q&A Session
    Introduction to OpenAI GPT-4
    - Architecture and capabilities
    - Use cases in testing
    Introduction to Llama 3.2
    - Features and differences from GPT-4
    Applications of Generative AI in Software Testing
    - Potential and limitations
    Mechanics of LLMs
    How LLMs Work: Transformers and Attention Mechanisms
    - Detailed explanation of transformer architecture
    - Role of attention mechanisms in LLMs
    Language Model Training Basics
    - Pre-training vs. fine-tuning
    - Data requirements and pre-processing
    Initial Interaction with GPT-4 and Llama 3.2
    - Setting up the environment
    - Exploring basic functionalities
    Ethical Use of LLMs in Testing
    - Bias and fairness
    Q&A and Wrap-up
    Crafting Effective Prompts
    Introduction to Prompt Engineering
    - Importance in maximizing LLM outputs
    Techniques for Effective Prompt Creation
    - Understanding context and intent
    - Avoiding common pitfalls
    Crafting Prompts for Testing Scenarios
    - Use cases specific to software testing
    Exercise: Creating Prompts for Test Data Generation
    - Group activities and discussions
    Exercise: Crafting Prompts for Test Cases
    - Individual practice with feedback
    Q&A and Wrap-up
    LLMs in Test Script Generation
    Generating Selenium Scripts Using LLMs
    - Step-by-step guide
    - Best practices
    Automating Test Script Creation
    - Integrating LLM outputs into automation frameworks
    Generating Test Scenarios with LLMs
    - Techniques and examples specific to retail banking and payments domain
    Hands-On Exercise: Refining Generated Scripts
    - Real-time problem-solving
    Q&A and Wrap-up
    Understanding Fine-Tuning
    Concepts of Fine-Tuning LLMs
    - Benefits and challenges
    Data Preparation for Fine-Tuning
    - Data collection, cleaning, and formatting
    Tools and Platforms for Fine-Tuning
    - Overview of popular tools
    Fine-Tuning GPT-4/Llama 3.2 for Testing Tasks
    - Practical implementation steps
    Exercise: Fine-Tuning a Model for a Specific Testing Domain
    - Guided project work
    Q&A and Wrap-up
    Introduction to Reinforcement Learning (RL)
    Basics of Reinforcement Learning
    - Key concepts and terminology
    Applying RL in Testing
    - How RL enhances testing strategies
    LLM-Powered Applications Using RL
    - Case studies and examples
    Implementing RL for Test Optimization
    - Setting up an RL environment
    - Developing a simple RL model for testing
    Q&A and Wrap-up
    Model Evaluation Techniques
    Metrics for Evaluating LLM Performance
    - Accuracy, perplexity, BLEU scores, etc.
    Testing and Validating LLM Outputs
    - Ensuring reliability and correctness
    Error Analysis and Troubleshooting
    - Identifying and fixing common issues
    Improving Models for Testing Tasks
    - Strategies for optimization
    Hands-On Exercise: Evaluating and Refining Models
    - Applying learned techniques
    Q&A and Wrap-up
    Case Studies and Best Practices
    Real-World Applications in Testing
    - Success stories and lessons learned
    Implementing LLMs in Existing Test Environments
    - Integration strategies and considerations
    Best Practices for LLM Integration
    - Maintenance and scalability
    Final Project Presentations
    - Students present their projects showcasing the application of course learnings
    Course Wrap-Up and Feedback
    - Summary of key takeaways
    - Collection of participant feedback

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FAQs

Who is this course for?
This course is specifically designed for:
  • - Manual Testers looking to transition into AI-driven automation roles.
  • - Automation Testers aiming to enhance their automation frameworks using Generative AI.
  • - Test Leads and Managers seeking innovative solutions to streamline testing cycles and reduce costs.
Participants should have experience/knowledge in Selenium and RestAssured.
Absolutely! You'll engage in hands-on projects like Selenium code generation, automatic defect reporting, and custom test data generation, gaining practical GenAI skills.
The course duration is 8 weekends, including interactive lectures, practical hands-on exercises, and project work.
Yes, participants will receive a course completion certificate from Testleaf that validates your expertise in Generative AI for QA.
Absolutely! All live sessions will be recorded, and you'll have full access to session recordings and course materials at your convenience.
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