The AI for QA course is a comprehensive, industry-aligned program designed to help software testers and QA professionals confidently adopt Artificial Intelligence in their testing workflows. As software systems grow more complex and release cycles become shorter, traditional testing methods alone are no longer sufficient. This course bridges that gap by combining AI fundamentals, modern automation tools, and real-world QA use cases.
The program begins by building strong conceptual clarity around Artificial Intelligence—covering Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), AI Assistants, and AI Agents. Participants gain a clear understanding of how modern AI systems work, why transformers changed the AI landscape, and how tools like ChatGPT and Copilot derive their intelligence.
Moving beyond theory, the course focuses on practical workplace adoption of AI. Participants learn why AI is essential in today’s QA roles and how to integrate it responsibly at individual, team, and enterprise levels. Special emphasis is placed on developing the right AI mindset, reducing fear, and identifying high-impact testing activities where AI delivers immediate value.
A dedicated section introduces Large Language Models (LLMs) in a simple, intuitive way, explaining concepts such as stochastic language processing, neural networks, tokens, and context windows. Learners then explore Retrieval-Augmented Generation (RAG)—a powerful approach that allows AI systems to retrieve information from internal documents, knowledge bases, and repositories to provide accurate, context-aware responses, making it highly relevant for enterprise QA environments.
The course also introduces modern AI application frameworks such as LangChain, LangGraph, and LangSmith, helping participants understand how AI-powered workflows and agents are built, monitored, and optimized in production systems. Advanced concepts like Model Context Protocol (MCP) are covered to explain how AI models interact seamlessly with tools, databases, and external systems.
From a QA perspective, the program deeply explores Generative AI-driven software testing. Participants learn effective prompt engineering techniques—including zero-shot, few-shot, chain-of-thought, and iterative prompting—to generate test cases, test plans, edge cases, and documentation with precision and consistency. The course also covers BDD, Cucumber, and Gherkin, ensuring AI-generated tests align with collaborative, business-readable formats.
Hands-on exposure to Playwright and Selenium helps learners understand where each tool fits and how AI enhances automation through integrations like GitHub Copilot. Topics such as agentic testing frameworks, AI-powered testing tools, self-healing tests, visual testing AI, and performance testing fundamentals provide a holistic view of the modern QA ecosystem.
By the end of the course, participants will be equipped to:
- Understand AI concepts with confidence and clarity
- Use AI tools effectively in daily QA tasks
- Design intelligent testing workflows using AI
- Improve test coverage, speed, and quality
- Prepare for the future of hybrid human–AI testing teams
This course is ideal for manual testers, automation testers, QA leads, and test managers who want to future-proof their careers and lead the transition toward intelligent, AI-assisted software testing.
Curriculum
- 3 Sections
- 9 Lessons
- 15 Weeks
- Introduction to AI4
- AI Terminologies and Deep Dive3
- AI in Software Testing2