AI adoption is not a prompt-writing contest
AI can generate test cases, summarise requirements, analyse data, and assist with many QA activities. But a convincing demo does not guarantee a reliable process.
  • How do you know whether AI is improving quality rather than just increasing output?
  • How do you prevent AI-assisted workflows from becoming inconsistent over time?
  • How do you justify an AI initiative — or explain why it is not worth implementing?
This course treats AI adoption as an engineering challenge. You will learn to evaluate use cases, define success criteria, manage risks, and build solutions around real project constraints.
What you will be able to do
  • Evaluate AI opportunities
    Map your QA process, identify promising use cases, and explain where AI adds value — and where it does not.
  • Measure real impact
    Define success criteria and metrics that go beyond generation speed. Evaluate quality, effort, reliability, and the cost of introducing AI.
  • Control AI-assisted workflows
    Design structured prompts, reusable skills and rules, verification mechanisms, and evaluation procedures.
  • Make informed adoption decisions
    Build a pilot proposal, communicate trade-offs to stakeholders, and make a defensible go/no-go decision.
  • Design AI agents for QA
    Translate a real QA use case into an agent architecture and define how to evaluate its performance.
Choose your learning path
Combine the strategic and implementation perspectives: decide where AI belongs in your QA process, then design and evaluate a solution for a selected use case.
  • Core: AI adoption in QA
    From experimentation to an informed decision
    Learn how to assess AI opportunities, manage risks, measure outcomes, and plan a pilot that fits your actual QA process.
    You will create:
    • An AI opportunity map
    • A risk register
    • A pilot agreement with success criteria
    • A stakeholder communication and decision framework
    Best for: QA engineers, QA leads, and quality managers who want a structured approach to AI adoption.
  • Advanced: Engineering AI-assisted QA
    From a use case to a controlled solution
    Design more reliable AI-assisted workflows using prompt architecture, reusable instructions, agent design, and systematic evaluation.
    You will create:
    • A prompt architecture and control toolkit
    • An AI agent architecture
    • An evaluation plan for assessing output quality
    Best for: practitioners who want to move beyond isolated AI experiments and design controlled, verifiable solutions.
Learn by working on a real QA problem
This is not a collection of disconnected demos. You will build a project step by step, using a use case from your own work.
  • Live sessions
    Practical webinars of approximately 90 minutes, with exercises and discussion.
  • Work on your project
    Apply each concept to your selected use case and build a set of artifacts you can adapt to your team.
  • Reusable materials
    Get written session notes, templates, and practical examples to use beyond the course.
  • Recordings and optional homework
    Revisit the sessions when you need to. Optional homework helps you develop your project between webinars, with opportunities for discussion or individual feedback depending on your learning format.
  • Tool-independent approach
    The course is not tied to a particular AI platform. You can choose tools that fit your access permissions, subscriptions, and information-security requirements.
What this course is — and is not
The goal is not to use more AI.
It is to make better decisions about where and how to use it.
  • It is practical
    You will work with use cases, risks, metrics, process design, prompt architecture, and evaluation.
  • It is not an AI theory course
    You do not need to study neural network architectures to benefit from the program.
  • It is not a tool-specific tutorial
    The focus is on transferable engineering practices, not on mastering one vendor's interface.
  • It is not about automating everything
    Sometimes the best decision is to limit AI usage or not introduce it at all.
A practical, structured learning experience
  • 2 learning tracks
    Core and Advanced — from adoption decisions to solution design.
  • 15 live sessions
    Seven sessions in Core,
    eight sessions in Advanced. Approximately 90 minutes each.
  • A project based on your own use case
    Build practical artefacts throughout the course and apply the concepts to your QA process.
  • Reusable templates and materials
    Take away structured approaches you can adapt to your team.
Meet your instructor
Natalia Petrovskaia
QA Engineering Leader · Quality Consultant ·
AI in QA Educator

I have 10+ years of experience in software testing and QA leadership, including building testing processes, developing test strategies, introducing automation, and leading teams of more than 30 QA and automation engineers.

My background spans Life Sciences, Healthcare, Finance, eCommerce, and EdTech, including complex enterprise environments with legacy systems, integrations, demanding release requirements, and tight deadlines, as well as startup environments that require fast decisions and rapid delivery.


My approach to AI in QA builds on the same principles I apply to quality engineering: understand the problem, evaluate the risks, define success criteria, and verify the results.

Contact me in LinkedIn

I created this course to help QA professionals move from scattered AI experiments to a deliberate, measurable, and controlled approach to adoption.


My principle is simple: AI should support engineering judgment, not replace it.

Find the format that works for you
Choose between self-study, live group learning, and individual feedback.
For those who prefer to learn at their own pace
Access course text materials, written session notes, templates, and practical artifacts. Live sessions and homework feedback are not included.
Select your learning path
Choose Core to assess AI opportunities and plan a responsible, measurable pilot.
Choose the Full course to progress from AI adoption decisions to designing AI-assisted QA solutions.

AI in QA: Core

Assess AI opportunities, manage risks, define metrics, and plan a pilot.

  • €150
    Self-study
  • €300
    Standard
  • €500
    Individual

AI in QA: Full

Progress from evaluating AI use cases to designing AI-assisted workflows, prompt architectures, and AI agents.

  • €250
    Self-study
  • €500
    Standard
  • €800
    Individual

Not sure which option is right for you?

Contact me to discuss your goals and choose the most suitable learning format.

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