AI is changing QA work in two directions at once. QA engineers can use AI to write test plans, generate automation scripts, and triage bugs faster. And more of the products they test now have LLMs or agents inside them, which need a different kind of testing: the same input can give different outputs, and “correct” is often a judgment call.
GenAI & Agentic AI for QA and Test Engineers is a 12-hour AI testing course that covers both. This guide walks through what’s in it and who it suits.
The AI testing course at a glance
| Format | Live online, three consecutive days, with hands-on labs in every module |
| Hours | 12 |
| Modules | 6: three generative AI use cases and three agentic AI use cases |
| Prerequisites | Python for AI (6 hours) and basic QA concepts: test case design and Selenium or Cypress |
| Extra cost | About $10 of API credit covers all labs |
| Assessment | A final test covering all six use cases, plus the labs; certificate of completion on passing |
| Current price | $450 or ₹25,000 |
Additional discounts are available. Contact us to know more.
What the six modules cover
| Module | Type | What you practice |
|---|---|---|
| Test plan and case generation | Generative AI | Turning a feature spec into positive, negative, and edge-case coverage |
| Automation scripts and self-healing tests | Generative AI | Generating Selenium, Cypress, and Playwright scripts with page objects and locators that adapt to UI changes |
| Bug report generation | Generative AI | Turning logs, screenshots, and steps into developer-ready reports with severity and likely root cause |
| Regression, performance, and security testing agents | Agentic AI | Agents that compare against baselines, run load tests, and check for OWASP Top 10 issues before release |
| AI and LLM evaluation testing | Agentic AI | Golden datasets, LLM-as-judge and human raters, DeepEval, RAGAS, and Promptfoo, plus hallucination, drift, and prompt injection tests |
| Bug triage and quality reporting agents | Agentic AI | Classifying severity, routing to teams, detecting duplicates, and reporting trends |
Using AI to test vs testing AI
Most of the course is about using AI to test conventional software faster. One module is about testing AI-powered products themselves. That balance matches where most QA teams are today, but it’s worth knowing before you enroll.
Number of modules
Source: AgileFever course curriculum, October 2026. Grouping is ours.
That one module is where QA work is changing most. Testing an LLM feature means building a golden dataset of real inputs and acceptable answers, deciding when an automated judge is trustworthy and when a person needs to rate outputs, and checking for failures that traditional test cases don’t catch: confident wrong answers, quality drifting after a model update, and inputs crafted to make the system ignore its instructions. If your team is about to ship AI features, that module alone is worth the time.
Who it’s for
| You are… | What you’ll use most |
|---|---|
| A manual tester | Test case and bug report generation from day one, and a path into automation |
| A QA automation engineer or SDET | Self-healing scripts, testing agents, and LLM evaluation frameworks |
| A test analyst | Test design with AI and owning the test strategy for AI features |
| A QA lead or test manager | Triage and quality reporting agents, and setting quality bars for AI features |
Who should take something else
- If you’ve never done any QA work, start with basic test design and a tool like Selenium or Cypress first
- If you want to build AI applications rather than test them, look at the GenAI and Agentic AI BootCamp
- If you need a deep course only on evaluating LLM systems, this covers the essentials in one module rather than in depth
Upcoming dates
| 2026 | 2027 |
|---|---|
| November 2 to 4 | January 4 to 6 |
| December 7 to 9 | February 1 to 3 |
| – | March 1 to 3 |
| – | April 5 to 7 |
Check the course page for time zones.
Additional discounts are available. Contact us to know more.
FAQ
Will AI replace QA engineers?
It automates a lot of repetitive test work, but not test strategy, risk judgment, or deciding whether a release is good enough. The role is shifting toward those things, and toward testing AI systems.
What are self-healing tests?
Tests that notice when a UI locator has changed and adapt, instead of failing every time a button moves. They cut down the maintenance that eats into automation work.
Do I need prior LLM experience?
No. The course introduces the APIs and tools you need.
What to read next
For more examples of AI in testing work, read GenAI and agentic AI use cases for QA and test engineers. For how product owners set quality bars for AI features, see Product Owner vs Product Manager: Who Owns the AI Roadmap?
