AI for Quality Assurance (QA)

AI is transforming QA – this 8-hour live virtual training teaches you to use it across the entire testing lifecycle, from generating BDD/Gherkin test cases and automation scripts to self-healing broken locators, creating synthetic test data, and driving AI-assisted bug triage. You’ll also learn to test and evaluate RAG systems for accuracy and hallucinations. Through hands-on workshops, you’ll leave with a personal prompt library, an automation framework, and a roadmap for scaling AI-driven QA across your team.

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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 Schedules

What is AI For Quality Assurance?

AI for Quality Assurance uses artificial intelligence, especially LLMs, to accelerate testing across the software lifecycle generating test cases, automating scripts, creating synthetic data, and triaging bugs faster. It acts as a force multiplier, not a replacement, freeing QA professionals to focus on exploratory testing and critical judgment. This shift enables teams to test smarter and at greater scale. Humans still own the key quality decisions and domain expertise.

Who can Join the AI For Quality Assurance?

This training is designed for anyone involved in software testing and quality who wants to work smarter with AI. It’s ideal for:

QA Engineers & QA Leads looking to modernize their testing approach
Test Automation Engineers & SDETs who want to automate scripting, self-healing, and maintenance
Release Managers who need better risk visibility and quality reporting
Anyone testing RAG or AI-powered systems looking to build evaluation and testing skills for these newer architectures

No advanced AI or coding expertise is required just a working knowledge of testing concepts and a willingness to experiment hands-on during the session.

Why choose TMSQUARE?

At TM Square, we believe training should transform careers, not just add a certificate. This AI for QA program is built around real-world, hands-on outcomes, so you walk away with practical skills you can apply from day one, not just theoretical knowledge of AI concepts. With a proven track record supporting global leaders like Amadeus, Deloitte, Bosch, KPMG, Siemens, and many more, and a presence spanning India, South Africa, and Australia, we combine international training standards with a deep understanding of what modern QA teams actually need to test smarter, faster, and at scale.

Course Overview

This 8-hour live virtual training helps QA professionals integrate AI across the testing lifecycle from prompting and BDD test generation to automated scripting, self-healing tests, and synthetic data creation. Participants also learn AI-assisted bug triage, quality reporting, and how to test and evaluate RAG systems for accuracy and hallucinations.

Through hands-on workshops and live demos, attendees apply these skills to real scenarios converting raw requirements into test case matrices, building and self-healing automated scripts, generating compliant synthetic datasets, and diagnosing pipeline failures with AI assistance. By the end of the session, participants leave with a personal AI prompt library, an AI-augmented test plan template, and an automation framework setup they can use immediately, along with a roadmap for scaling AI adoption safely across their QA teams.

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.

Module 1: Why AI, Why Now for QA Professionals

Icebreaker: “How are you currently leveraging AI in your test cycles, and where do bottlenecks remain?”

Understanding AI as a testing force multiplier, not an automated replacement for human exploratory testing and QA judgment

Where AI fits across the testing lifecycle: test case generation, test data creation, script maintenance, bug triage, and regression optimization

What AI cannot do: understand intuitive end-user friction, own release go/no-go decisions, or replace deep exploratory domain expertise

Module 2: AI Foundations & Testing Prompting

LLMs and test-generation models explained: context windows, token constraints, and handling complex technical specs

Understanding hallucinations and flake factors in AI-generated tests and assertions

Prompting fundamentals for QA: specifying acceptance criteria, constraints, expected data states, and target testing frameworks

Live demonstration: Weak Prompt (generic test cases) vs. Strong Prompt (comprehensive BDD/Gherkin scenarios with boundary conditions)

Hands-on activity: Craft and iterate an AI prompt to turn a vague user story into a complete test specification

Module 3: AI-Driven Test Case Generation & BDD

Using AI to extract test scenarios directly from requirements, wireframes, and PRDs

Generating positive, negative, and boundary-value test cases automatically

Converting functional specs into BDD (Behavior-Driven Development) scripts and Gherkin syntax

Identifying hidden gaps, logical contradictions, and edge cases in product requirements prior to coding

Workshop: Transform raw product documentation and acceptance criteria into a rigorous matrix of test cases

Module 4: AI for Test Automation & Script Self-Healing

Generating automated test scripts (e.g., Selenium, Playwright, Cypress, Appium) via LLMs

AI-assisted test maintenance: fixing broken locators, dynamic selectors, and flaky test logic

Using AI to translate manual test procedures into automated scripting frameworks

Refactoring and modularizing legacy test suites for better readability and reduced execution overhead

Workshop: Build and execute automated test scripts and self-heal broken locators using AI tools

Module 5: Synthetic Test Data Generation & Mocking

Generating realistic synthetic test datasets for compliance, performance, and boundary stress testing

Avoiding PII and data privacy violations through intelligent data masking and synthetic simulation

Generating complex payloads, database fixtures, and edge-case mock responses for API testing

Simulating high-concurrency states and error payloads using AI-generated mock services

Activity: Generate a compliant, multi-variant synthetic test dataset for a complex financial or e-commerce workflow

Module 6: AI-Assisted Bug Triage, Root Cause Analysis & Reporting

Analyzing flaky test results, CI/CD pipeline logs, and crash dumps with AI for rapid root-cause identification

Automating bug report generation: formatting clear reproduction steps, logs, and environment details from raw error outputs

Clustering, deduplicating, and prioritizing incoming bug reports across massive test runs

Generating executive-ready quality dashboards, test summaries, and release risk assessments

Activity: Diagnose a complex pipeline failure and format a structured bug triage report using AI assistance

Module 7: Testing & Evaluating RAG (Retrieval-Augmented Generation) Systems

Understanding RAG architecture: retrievers, vector databases, embeddings, chunking strategies, and the generation layer — and where each can fail

Testing retrieval quality: relevance, recall/precision of retrieved chunks, and detecting stale or missing context in the knowledge base

Evaluating groundedness and faithfulness: catching hallucinations, unsupported claims, and answers that drift from the retrieved source material

Building automated RAG evaluation pipelines: golden datasets, retrieval-vs-generation regression checks, and end-to-end quality scoring

Activity: Design a test suite that isolates whether a RAG system's errors come from retrieval or generation

Module 8: Building Your AI-Enabled QA Toolkit & CI/CD Pipelines

Integrating AI tools and plugins directly into QA workflows, IDEs, and CI/CD testing pipelines

Overview of autonomous agentic testing workflows and AI-driven exploratory testing bots

Building a personal repository of reusable QA prompts and test-generation templates

Creating a team adoption roadmap for scaling AI-driven quality assurance safely across squads

Deliverable: Each participant leaves with an AI-augmented test plan template, personal prompt library, and automation framework setup

Session wrap-up: Q&A, feedback, and close

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