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AI is changing QA in two directions. QA engineers can now use AI to generate tests, automate regression, triage bugs, and reduce repetitive work. At the same time, teams are shipping products powered by LLMs and AI agents that need to be tested differently from traditional software.
This certification prepares you for both. You will learn to work faster as a QA engineer and evaluate whether AI-powered features are reliable, safe, and production-ready using golden datasets, LLM-as-judge methods, hallucination and drift testing, and real evaluation frameworks.
Create Test Plans Faster - Turn a feature specification into positive, negative, and edge-case coverage in minutes instead of hours.
Automate More of Your Testing - Generate Selenium, Cypress, and Playwright scripts with self-healing patterns that reduce recurring locator maintenance.
Write Better Bug Reports - Turn logs, screenshots, and reproduction steps into developer-ready reports with severity and root-cause context.
Run Testing Agents - Deploy AI agents for regression, performance, and OWASP security checks before every release.
Test AI-Powered Features - Build golden datasets and evaluate LLM and agent outputs using LLM-as-judge and human evaluation methods.
Detect AI Failures Before Production - Test for hallucination, drift, and prompt injection instead of discovering AI failures through customers.
Automate QA Intelligence - Use agents to classify bugs, detect duplicates, route issues, and surface quality trends automatically.
OUTCOME
Generate a complete test plan with edge-case coverage from a feature spec in minutes.
TIME SAVED
Hours of manual case-writing → minutes
OUTCOME
Generate automation scripts that adapt to UI changes instead of breaking every sprint.
TIME SAVED
Eliminates recurring locator-fix maintenance
OUTCOME
Ship bug reports developers can act on immediately, with no back-and-forth for reproduction steps.h
TIME SAVED
Cuts triage back-and-forth significantly
OUTCOME
Deploy agents that run regression, performance, and security checks automatically before every release.
TIME SAVED
Catches issues before production, not after a customer reports them
OUTCOME
Build a golden dataset and run it through a real evaluation framework, the skill behind the AI QA pay premium.
TIME SAVED
Avoids shipping AI features that fail silently in production
OUTCOME
Automate bug triage and quality reporting so trends are visible to stakeholders without manual tracking.
TIME SAVED
80%+ of bugs auto-triaged
I’d been doing automation for six years, but I’d never built a golden dataset before this course. Now when my team ships an LLM feature, I’m the one who knows how to actually evaluate it, not just click through it manually.
The self-healing test module alone saved my team hours every sprint. We used to lose half a day every release just fixing broken locators.
I was a manual tester for years and assumed this course would be over my head. It wasn’t — the test plan and bug report modules were immediately useful, and the eval module gave me a real path into automation.
AI can automate parts of test execution and repetitive QA work, but it does not replace the judgment required for test strategy, quality decisions, debugging, and risk assessment. The role shifts toward test strategy, quality architecture, and validating AI-powered systems.
AI test automation uses AI to test traditional software faster, such as generating scripts and running regression tests.
Testing AI-powered products means testing the LLM or agent itself using golden datasets, evaluation frameworks, human evaluation, and tests for hallucination and drift.
This certification teaches both.
You need basic familiarity with QA automation concepts and tools such as Selenium, Playwright, or similar frameworks. You don’t need prior AI experience. Python for AI is the prerequisite.
Yes. Manual testers can immediately use AI for test-case generation and bug-report creation. The automation and AI evaluation modules also provide a path toward expanding into more AI-enabled QA responsibilities.
Self-healing tests use AI to identify changes to UI locators and adapt test scripts accordingly, reducing the repeated manual work of fixing tests after UI changes.
Yes. The AI/LLM Evaluation module covers golden datasets, LLM-as-judge, human raters, DeepEval, RAGAS, Promptfoo, hallucination, drift, and prompt-injection testing.
You’ll be able to generate test plans faster, automate tests, use self-healing approaches, deploy testing agents, evaluate AI outputs, detect AI-specific failures, and automate bug triage and quality reporting.
No. Prior OpenAI or LLM API experience is not required. The course introduces the required concepts and tools during the program.
The curriculum includes workflow examples such as hours of test-case writing reduced to minutes, elimination of recurring locator fixes, and significant reductions in bug triage effort. These are practical course workflow examples, not guaranteed individual results.
You will receive the AgileFever Certificate of Completion after completing the hands-on labs and passing the final assessment.
