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PRC1002 – Certified GenAI-Assisted Test Engineer (GenAiA-TE) (4 Days)

Description

PRC1002 – Certified GenAI-Assisted Test Engineer (GenAiA-TE) (4 Days)

Overview

The Certified GenAI-Assisted Test Engineer (GenAiA-TE) program is a four-day certification course designed to equip software testing professionals with practical knowledge and hands-on experience in applying Generative AI (GenAI) to modern testing practices.

This program introduces participants to GenAI-assisted testing across the software development lifecycle, including prompt engineering, requirements review, test design, test data generation, bug advocacy, and AI adoption strategies in testing. Through structured learning and practical application, participants will learn how to leverage Large Language Models (LLMs) to enhance testing effectiveness, efficiency, and quality while preparing for AI-driven testing environments.

  • 92% positive reviews (221)
  • 2139 students

This program can be tailored to your specific business needs.

A little bit of personalization goes a long way. Ask us for a no-obligation Training Needs Analysis (TNA) so we can tailor this, or any other CSG program to meet your learning outcomes.

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Objectives

At the end of the training, participants would be able to:

  • Apply GenAI techniques in software testing activities
  • Use effective prompt engineering techniques for testing tasks
  • Conduct GenAI-assisted requirements review
  • Design and optimize test cases using AI-supported approaches
  • Generate test data using GenAI techniques
  • Improve bug advocacy and reporting using NLP-driven methods
  • Understand AI adoption strategies and future directions in testing

4 Days

  • Test Engineers
  • QA Engineers
  • Software Testers
  • Test Leads
  • Professionals involved in software testing and quality assurance
  • Shape

Modules:

GenAI-Assisted Testing
  • Introduction to Artificial Intelligence and Large Language Models (LLMs)
  • Evolution of software testing
  • Benefits and challenges of GenAI in testing
  • Fundamentals of prompting
  • Common prompting patterns
  • Using Markdown for clarity and structure
  • Adapting prompts to changing contexts
  • Mental models for effective prompting
  • LLM evaluation and calibration
  • Reviewing prose and data requirements using GenAI
  • Formats and techniques for AI-assisted requirements validation
  • Systematic test generation techniques
  • Exploratory and non-functional testing
  • Diagram-based and linguistics-based testing
  • Data representation techniques
  • Fuzzing and regular expression (Regex) approaches
  • Writing clear and effective bug reports
  • Applying NLP techniques to improve bug reporting
  • Custom GPTs and AI configurations
  • Developing an AI adoption strategy for testing teams

Program Methodology

  • Hands-on Activities: Practical exercises to reinforce theoretical concepts.
  • Group Discussions: Opportunities for peer-to-peer learning and exchange of ideas.
  • Role Plays: Simulations of realistic situations to build practical skills.
  • Feedback Sessions: Reviews and reflections to encourage improvement.
  • Problem-solving Exercises: Develop critical thinking and decision-making skills.
  • Experiential Learning: Learning by doing, promoting active involvement.
  • Interactive Lectures: Engaging presentations by experts in the field.
  • Case Studies: Real-world scenarios for learners to apply their knowledge.
  • Quizzes & Tests: Regular assessments to track learning progress.

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