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AI-Driven QA

Training Session

Advisory & Training

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Offering Overview

AI-Driven QA is a focused two-day consulting and training engagement designed to help QA engineers, SDETs, and development teams apply AI practically across modern testing practices. The course covers prompt engineering fundamentals, GitHub Copilot for test generation and automation, Model Context Protocol (MCP) integration with Azure DevOps and Playwright, and complete end-to-end workflows from requirements to CI/CD.

The sessions combine structured learning with hands-on exercises, ensuring teams leave with immediately applicable skills in AI-assisted test case design, Playwright automation, and intelligent CI/CD analysis rather than abstract theory.

Value

Quality assurance is often one of the most time-intensive stages of the SDLC. By introducing AI-assisted testing techniques, teams can significantly improve test coverage, reduce manual effort, and detect defects earlier in the delivery cycle.

Key benefits include:

  • Faster test case generation through prompt engineering and GitHub Copilot
  • Improved test automation coverage using AI-assisted Playwright development with Page Object Model
  • Reduced maintenance overhead through intelligent test refactoring and flaky test detection
  • Earlier defect detection by AI-powered CI/CD failure analysis and build diagnostics via MCP

This offering helps teams modernize QA without replacing existing tools or frameworks, instead augmenting Playwright and Azure DevOps with AI-driven capabilities while maintaining human oversight and quality control.

Approach

DAY 1 - Foundations & AI Assessment QA (480 min)

1. Introduction & Training Goals (30 min)

  • Understand the role of AI in QA
  • Human-in-the-loop principle
  • Participant expectations

2. AI in Quality Assurance – Fundamentals (60 min)

  • AI in SDLC
  • Traditional QA vs AI-assisted QA
  • What AI can and cannot do in testing
  • Limitations and risks of using AI

3. Break (15 min)

4. Art of the Prompt Engineering for QA (75 min)

  • Structure of an effective prompt
  • Prompting patterns in QA
  • Iterative prompt refinement
  • Common prompt mistakes

5. Lunch Break (60 min)

6. GitHub Copilot in Manual QA process (90 min)

  • Test case generation
  • BDD / Gherkin scenarios
  • Risk-based testing
  • Regression checklists

7. Break (15 min)

8. GitHub Copilot in Test Automation (Playwright) (105 min)

  • Generating Playwright tests
  • Explaining and refactoring tests
  • Page Object Model supported by AI

9. Day 1 Wrap-Up (30 min)

  • Key takeaways
  • Q&A and discussion
  • Preparation for Day 2
DAY 2 - MCP, CI/CD & End-to-End Workflow (480 min)

1. Recap of Day 1 (15 min)

  • Review of key concepts from Day 1 and Q&A

2. Model Context Protocol (MCP) – Concepts & Setup (85 min)

  • Key concepts of the MCP
  • Architecture: AI + tools
  • MCP servers and available tools
  • MCP integration with VS Code

3. Break (15 min)

4. MCP for Azure DevOps (70 min)

  • Access to pipelines and builds
  • Analyzing CI/CD failures with AI
  • Reading and summarizing test results
  • Automatic creation of bugs and work items

5. Lunch Break (60 min)

6. MCP for Playwright – AI-powered Test Automation (85 min)

  • DOM analysis with AI
  • Generating tests from the functional requirements
  • Flaky test detection and stabilization
  • Test refactoring using MCP

7. Break (15 min)

8. End2End QA x AI Workflow (Workshop) (85 min)

  • Workflow: requirements → tests → automation → CI/CD → reporting
  • Integration of Copilot + MCP + Playwright + Azure DevOps
  • Complete scenario exercise

9. Best Practices, Risks & Security (30 min)

  • Quality control of AI-generated artifacts
  • Avoiding over-reliance on AI

10. Close & Final Q&A (15 min)

  • Summary of outcomes
  • Open discussion
  • Next steps and follow-on support options

+ You Will Receive:

  • A clear understanding of how AI can accelerate testing across the full QA lifecycle

  • Practical skills in prompt engineering tailored to QA scenarios and test automation

  • Hands-on experience with GitHub Copilot for test case generation and Playwright automation

  • Working knowledge of Model Context Protocol (MCP) integrated with Azure DevOps and Playwright

  • One or more complete AI-assisted QA workflows from requirements to CI/CD integration

  • Best practices and guardrails for responsible AI adoption in testing environments

  • A structured path to modernize your QA process while maintaining quality standards and team control

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