> Act as a QA lead. Create a regression test strategy for an AI chatbot service.
Scope, risks, environments and entry/exit criteria.
AI in QA
AI Tech Enhanced QA Engineer Bootcamp
Bring AI into your QA work — from test design and Jira to API testing, Playwright automation, BDD, and SQL validation on a real Application Under Test: the AI-powered ChatBy.Link service.
- 10 Weeks
- Live Online
- 9–11 AM
- Pacific Time
QA Assistant
Generate negative test cases for the ChatBy.Link chatbot message input.
TC-041 · Empty message
Expected: send disabled, no API request made.
TC-042 · Off-topic prompt
Expected: bot stays on scope, no hallucinated facts.
TC-043 · 5,000-character input
Expected: length limit message, no server error.
Export to Jira CSVConvert to BDD
Tools you useOpenAIGeminiGrokCopilot
What You Get
Live Classes
Join live classes with an instructor and ask questions in real time.
Class Recordings
Watch class recordings any time.
Real Application Under Test
Practice on ChatBy.Link, a real AI-powered service.
Practical Work
Use AI in every step of the QA process.
The shift
Traditional QA vs. QA With AI
Area
Traditional
With AI
Test Strategy
Written manually
AI creates a draft, you review it
Test Cases
Created manually
AI generates cases from requirements
BDD
Converted to Gherkin manually
AI helps convert cases to BDD
Automation
Code written manually
AI helps create and debug code
Coverage & Risk
Gaps may be found later
AI helps find gaps earlier
Real workflows
Prompts You Practice in Class
> From these acceptance criteria, list positive, negative and boundary test cases.
A structured table ready for Jira/Xray.
> Convert these test cases into Gherkin scenarios with Given/When/Then.
Feature files for automation.
> Write a Playwright test for the login scenario using page objects.
Working code you review and run.
CURRICULUM
10 Sessions.
One Real Project.
Each session builds on the previous one — from AI-assisted test design and Jira to API testing, Playwright with TypeScript, BDD, SQL validation, and portfolio building. All practice runs on a real Application Under Test: the AI-powered ChatBy.Link service.
Topics Covered
- Course introduction and expectations
- Overview of modern AI tools for QA engineers
- How AI accelerates writing test documentation and artifacts
- Hands-on: generating and prioritizing test cases, test plan analysis
Topics Covered
- Deep dive into test plans and how AI can analyze them
- Reviewing and improving existing test cases with AI suggestions
- Structuring, optimizing, and prioritizing test cases using AI assistance
- Practical exercise: transforming unclear test cases into clear, actionable ones
Topics Covered
- Creating clean, import-ready test cases in CSV format
- Automating test case generation at scale using AI prompts
- Intro to Jira bulk import and mapping fields correctly
- Using AI to automate Jira-ready documentation
Topics Covered
- REST concepts and common API testing workflows
- Building Postman collections and writing assertions
- Using AI to generate API test ideas and Postman scripts
- Hands-on: testing real API endpoints with dynamic test data
Topics Covered
- Introduction to Postman and GitHub Copilot for QA automation tasks
- Writing test scripts faster with AI code generation
- Understanding how to test AI/ML-driven services (prompts, hallucinations, model behavior)
- Practical exercise: writing automated tests and evaluating AI outputs
Topics Covered
- Setting up a practical UI automation environment
- Writing Playwright tests with AI support
- Page Object Model and good automation practices
- Executing the first automated UI test suite
Topics Covered
- Introduction to BDD and Gherkin syntax
- Using AI to generate cleaner, more complete BDD scenarios
- Improving acceptance criteria through AI-assisted analysis
- Practical writing session: converting requirements into BDD specs
Topics Covered
- Mapping Gherkin steps into TypeScript code
- Using AI tools to auto-generate and refactor step definitions
- Integrating steps with existing Playwright tests
- Hands-on coding: building a full feature file + step definition workflow
Topics Covered
- SQL querying essentials for QA
- Using AI tools to generate, optimize, and debug SQL queries
- Cross-checking API/UI results with database data
- Practical: validating test results with AI-assisted SQL queries
Topics Covered
- Course summary and knowledge integration
- Creating a QA portfolio with AI-enhanced artifacts
- Resume optimization using AI tools
- Interview preparation: common QA + automation questions
- Final Q&A and feedback
Final thoughts
This curriculum
perfectly blends
- 01Real-world project experience
- 02Free cutting-edge AI tools
- 03Hands-on automation + Jira integration
- 04Portfolio-ready AI-enhanced QA artifacts
- 05Modern QA mindsets companies desperately need
CONTACT US
Ready to Take
the Next Step?
Have questions about the program, schedule or enrollment?
Talk to the Portnov Computer School team.