GenAI for QA: Masterclass in Testing Automation Course

Lesson 1 • What Generative AI Actually Does

Covers LLM mechanics, token prediction, and prompt-response cycles at a conceptual level. Grounds all later GenAI tooling decisions in accurate mental models.

Lesson 2 • Evaluating GenAI Output Quality

Introduces criteria for judging AI-generated artifacts: accuracy, coverage, and relevance. Prevents uncritical acceptance of AI output from the start.

Lesson 3 • GenAI Use Cases Across the Test Lifecycle

Surveys every stage where GenAI adds measurable value, from requirements analysis to post-release monitoring. Provides a reference map used throughout the course.

Lesson 1 • Anatomy of an Effective Prompt

Breaks down role, context, instruction, format, and constraint components of a prompt. Gives QA engineers a repeatable structure for any testing request.

Lesson 2 • Prompting Patterns for Test Generation

Applies chain-of-thought, few-shot, and template-based patterns specifically to test case creation. Connects prompt design directly to test coverage outcomes.

Lesson 3 • Iterative Prompt Refinement

Teaches systematic prompt debugging: identifying vague output, adjusting variables, and re-evaluating results. Builds a disciplined improvement loop for QA prompts.

Lesson 4 • Prompt Libraries and Reusability

Covers building, organising, and sharing a team-level prompt library for QA tasks. Scales individual prompt skills into a shared organisational asset.

Lesson 1 • Generating Functional Test Cases

Applies GenAI to produce positive, negative, and edge-case functional tests from specifications. Demonstrates quality checks to filter and refine AI output.

Lesson 2 • Reviewing and Validating AI-Generated Tests

Establishes a structured human-review process to catch gaps, duplicates, and incorrect assumptions in AI output. Closes the quality loop on AI-assisted design.

Lesson 3 • Equivalence Partitioning and Boundary Analysis

Uses GenAI to automate classical test design techniques at scale. Connects AI speed with proven coverage strategies for thorough functional testing.

Lesson 4 • Test Case Structuring and Documentation

Formats AI-generated tests into standardised templates compatible with test management tools. Ensures traceability from requirement to executable test.

Lesson 1 • UI Test Automation with GenAI

Generates browser-based UI test scripts including locator strategies and assertion logic. Addresses common fragility issues in AI-generated UI automation.

Lesson 2 • Translating Test Cases into Automation Code

Converts structured test cases into framework-specific scripts using targeted GenAI prompts. Bridges the gap between test design and executable automation.

Lesson 3 • API Test Automation with GenAI

Produces API test scripts covering request construction, response validation, and error handling. Connects to contract testing and schema validation concepts.

Lesson 4 • Code Review and Refactoring of AI Scripts

Applies GenAI to review its own output for bugs, redundancy, and maintainability issues. Establishes a self-improving code quality loop for automation assets.

Lesson 1 • Synthetic Personally Identifiable Information

Generates realistic but fictitious personal data that satisfies format requirements without privacy risk. Addresses compliance with data protection principles in test environments.

Lesson 2 • Test Data Challenges and GenAI Solutions

Diagnoses common test data problems: scarcity, sensitivity, and lack of diversity. Positions GenAI as a scalable solution for each problem type.

Lesson 3 • Test Data Management and Refresh

Establishes workflows for versioning, refreshing, and retiring test datasets generated by AI. Prevents data drift and ensures test repeatability over time.

Lesson 4 • Data Masking and Anonymisation

Uses GenAI to transform production data into safe test datasets through masking and anonymisation. Balances data utility with privacy protection requirements.

Lesson 1 • Defect Prioritisation and Risk Scoring

Ranks defects by business impact, frequency, and fix complexity using AI-assisted scoring. Enables data-driven triage decisions aligned with release risk.

Lesson 2 • Defect Trend Analysis and Prevention

Mines historical defect data with GenAI to identify recurring patterns and systemic weaknesses. Shifts QA from reactive triage to proactive defect prevention.

Lesson 3 • Root-Cause Analysis with GenAI

Applies GenAI to correlate failure symptoms with probable root causes across system layers. Reduces manual investigation time and improves fix accuracy.

Lesson 4 • Automated Defect Detection Patterns

Uses GenAI to scan logs, test results, and code diffs for defect signals. Establishes detection patterns that surface issues earlier in the development cycle.

Lesson 1 • Analysing Performance Test Results with AI

Uses GenAI to interpret performance metrics, identify bottlenecks, and suggest optimisations. Accelerates the analysis phase of performance testing cycles.

Lesson 2 • Chaos and Resilience Testing Scenarios

Generates failure injection scenarios and resilience test plans using GenAI for distributed systems. Prepares systems for unexpected conditions through structured chaos experiments.

Lesson 3 • Security Testing Augmentation with GenAI

Applies GenAI to generate security test cases, attack payloads, and vulnerability checklists. Broadens security coverage without requiring deep penetration testing expertise.

Lesson 4 • AI-Assisted Performance Test Design

Generates load profiles, virtual user scripts, and performance scenarios using GenAI. Connects performance test design to realistic usage patterns and SLA targets.

Lesson 1 • Assessing Organisational AI Readiness

Evaluates team skills, tooling maturity, and process readiness for GenAI adoption in QA. Produces a gap analysis that drives the implementation roadmap.

Lesson 2 • Governance, Ethics, and Risk Management

Establishes policies for responsible GenAI use in QA, covering bias, data privacy, and output validation. Protects the organisation from AI-related quality and compliance risks.

Lesson 3 • Designing the AI-Augmented QA Workflow

Architects end-to-end QA workflows with GenAI touchpoints at each stage. Balances automation with human oversight to maintain quality accountability.

Lesson 4 • Scaling and Continuous Improvement

Builds feedback loops and scaling mechanisms to grow GenAI QA capabilities over time. Ensures the strategy evolves with advancing AI capabilities and team maturity.