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From Prompts to Production: AI for QE Teams

From Prompts to Production: AI for QE Teams is an 8-hour instructor-led training delivered in virtual and in-person formats for Quality Engineering and testing professionals. The course provides a practical introduction to Generative AI and LLMs, prompt engineering for testing, AI-assisted QE workflows, risk management, and modern AI-enabled testing approaches including RAG and AI agents. Through hands-on exercises and real-world examples, participants will learn how to apply AI effectively across the software testing lifecycle.

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3.8 | 400+ Reviews
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Course Includes

  • 8 Hrs of Sessions
  • 24/7 Lifetime Support
  • One-on-one doubt clearing
  • Hands-On Driven
  • Certification Based Curriculum
  • Career path guidance
  • Real Time Users
  • Flexible

Why Choose TM SQUARE?

We have delivered the From Prompts to Prduction: AI for QE Teams program to over 1200+ professionals, focusing on practical, application-oriented learning. Our sessions are designed to be engaging, activity-based, and tailored to real-world challenges. With an average participant rating of 3.8/5, the program is consistently recognised for its clarity, relevance, and impact. Join this course to experience the expertise of our professional instructors and best experience of learning concepts.

 

Course Overview

From Prompts to Production: AI for QE Teams is a practical course that introduces Quality Engineering professionals to applying Generative AI across the software testing lifecycle. Participants will learn AI and LLM fundamentals, prompt engineering techniques, AI-assisted testing workflows, risk management, and modern concepts such as RAG and LLM-powered agents to enable smarter, scalable, and production-ready testing practices.

 

Audience

The qualification is intended for anyone involved in using generative AI for software testing, including testers, test analysts, test automation engineers, test managers, user-acceptance testers and software developers. It is equally valuable to professionals who need a solid understanding of GenAI in testing—project managers, quality managers, software development managers, business analysts, IT directors and consultants.

 

Course Outline

What is your return policy?

We offer a 30-day return policy from the date of purchase.

Do I need a receipt to return an item?

Yes, a valid receipt or proof of purchase is required.

Can I return online purchases in store?

Yes, online purchases can be returned in any of our physical stores.

How long does shipping take?

Standard shipping typically takes 3–5 business days.

Do you ship internationally?

Yes, we ship to most countries worldwide. Additional charges may apply.

Which countries are excluded?

We currently do not ship to embargoed countries or regions with postal restrictions.

Chapter 1 - Introduction to Generative AI for Software Testing

Generative AI Foundations and Key Concepts
AI Spectrum: Symbolic AI, Classical Machine Learning, Deep Learning, and Generative AI
Basics of Generative AI and LLMs
Foundation, Instruction-Tuned and Reasoning LLMs
Multimodal LLMs and Vision-Language Models
Leveraging Generative AI in Software Testing: Core Principles
Key LLM Capabilities for Test Tasks
Chapter 2 - Prompt Engineering for Effective Software Testing

Effective Prompt Development
Structure of Prompts for Generative AI in Software Testing
Core Prompting Techniques for Software Testing
System Prompt and User Prompt
Applying Prompt Engineering Techniques to Software Test Tasks
Test Analysis with Generative AI
Test Design and Test Implementation with Generative AI
Automated Regression Testing with Generative AI
Test Monitoring and Test Control with Generative AI
Choosing Prompting Techniques for Software Testing
Evaluate Generative AI Results and Refine Prompts for Software Test tasks
Metrics for Evaluating the Results of Generative AI on Test tasks
Techniques for Evaluating and Iteratively Refining Prompts
Chapter 3 - Managing Risks of Generative AI in Software Testing

Hallucinations, Reasoning Errors and Biases
Hallucinations, Reasoning Errors and Biases in Generative AI
Identify Hallucinations, Reasoning Errors and Biases in LLM Output
Mitigation techniques of GenAI hallucinations, reasoning errors and biases in software test tasks
Data Privacy and Security Risks of Generative AI in Software Testing
Data Privacy and Security Risks Associated with Using Generative AI
Data Privacy and Vulnerabilities in Generative AI for Test processes and Tools
Mitigation Strategies to Protect Data Privacy and Enhance Security in Testing with Generative AI
Chapter 4 - LLM-Powered Test Infrastructure for Software Testing

Architectural Approaches for LLM-Powered Test Infrastructure
Key Architectural Components and Concepts of LLM-Powered Test Infrastructure
Retrieval-Augmented Generation
The Role of LLM-Powered Agents in Automating Test processes
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