Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We teach this course. Skills and modules come from its program page, checked on October 26, 2026. We do not quote its time-saving claims because we cannot verify them for your team.
AI changes QA in two directions at once. The tools can write test cases and scripts, and the products being tested increasingly contain language models whose output varies from run to run. A tester has to be able to do the first and judge the second. This post lists each skill, where it is taught, and what is left for you to practise.
The six skills
| Skill | What you do | Module | Direction | Left for you to practise |
|---|---|---|---|---|
| Test plan and case generation | Turn a feature spec into positive, negative and edge-case coverage | 1 | AI for QA | Checking that generated cases match real requirements |
| Automation scripts and self-healing tests | Generate Selenium, Cypress or Playwright scripts with a Page Object Model and self-healing locators | 2 | AI for QA | Maintaining scripts in your own framework |
| Bug reports | Turn logs, screenshots and reproduction steps into a report with severity and root-cause context | 3 | AI for QA | Your team’s triage rules and severity scale |
| Regression, performance and security agents | Agents for regression with baseline comparison, load testing, and OWASP Top 10 checks | 4 | AI for QA | Wiring agents into your pipeline |
| AI and LLM evaluation testing | Golden datasets, LLM-as-judge against human raters, DeepEval, RAGAS and Promptfoo, hallucination, drift and prompt-injection testing | 5 | Testing AI | A golden dataset for your own product |
| Bug triage and quality reporting agents | Automatic severity classification and routing, duplicate detection, quality dashboards and trends | 6 | AI for QA | Calibrating it to your backlog |
6 modules
Only one module in six is about testing AI products, and that one is the most specific to this moment. Module 5 builds a golden dataset and runs it through a real evaluation framework, and it covers the failure types that traditional test cases miss: hallucinated facts, behaviour drifting between releases, and prompt injection. If your product ships an LLM feature, that module is the reason to take the course. If it does not, modules 1 to 4 and 6 are the main value.
What to expect from AI-generated tests
A generated test plan can be thorough and still miss the one case that matters, because the model does not know your users or your history of defects. The practical habit is to treat AI output as a first draft and review it as you would a junior colleague’s work. The course frames its outcomes the same way. Test cases and scripts are generated, and you decide what goes into the suite.
Prerequisites and assessment
| Item | Detail |
|---|---|
| Before you start | Python for AI (6 hours); comfort with QA basics such as test case design and Selenium or Cypress |
| LLM experience | Not required. The page says the course covers it |
| Cost of labs | About $10 of API credit covers all hands-on labs, according to the page |
| Assessment | Hands-on labs in each module and a final test covering all six use cases |
| Certificate | AgileFever Certificate of Completion on passing |
Course facts
| Item | Detail |
|---|---|
| Fee (US) | $450, plus about $10 of API credit |
| Monthly EMI | From about $19 a month through finance partners |
| Format | Live virtual, 12 hours over 3 days, Monday to Wednesday |
| Next cohort listed | November 2 to 4, 2026, 9:30 AM to 1:30 PM Eastern |
| Role transitions on the page | QA engineer to AI-augmented QA engineer; SDET to AI-native SDET; QA lead to AI quality engineering lead; manual tester to AI-powered automation tester |
Additional discounts are available. Contact us to know more.
Where the course stops
- It is not a test automation course. It assumes you already know Selenium or Cypress.
- It does not teach model training or evaluation theory beyond what testers use.
- It cannot decide your quality bar for AI features. That is a decision for your team and your product owner.
For a longer career view, read QA Engineer to AI Test Engineer and the use cases for QA and test engineers. Fees and what is included are in the course fees post. The program page is GenAI & Agentic AI for QA and Test Engineers.
Questions people ask
Do I need to know LLM APIs already?
No. The page says no prior OpenAI or LLM API experience is required, and the course covers it.
Is it useful if my product has no AI features?
Yes for modules 1 to 4 and 6, which use AI to speed up normal QA work. Module 5 matters most if you test LLM-based features.
Are sessions recorded?
Yes. Live sessions are recorded.
