Generative AI Course in Software Testing and Documentation

Lesson 1 • Ethical and Quality Risks of AI in QA

Identifies risks introduced when AI generates test artifacts. Prepares students to apply critical judgment before accepting AI output.

Lesson 2 • Key AI Concepts for QA Professionals

Translates AI terminology into QA-relevant meaning. Connects model behaviour patterns to testing implications students will encounter throughout the course.

Lesson 3 • AI Tools Landscape in Software Testing

Surveys the categories of AI tools used in testing and documentation workflows. Helps students identify which tool type fits which testing task.

Lesson 1 • Iterative Prompt Refinement

Teaches a structured loop for improving prompts based on output quality. Builds the habit of treating prompts as artifacts that require versioning and review.

Lesson 2 • Evaluating and Validating AI Output

Establishes criteria for judging whether AI-generated testing content is usable. Connects output evaluation to professional QA standards introduced in Chapter 1.

Lesson 3 • Anatomy of an Effective Prompt

Breaks down the structural components of a well-formed prompt. Provides a reusable framework students apply to every AI interaction in this course.

Lesson 1 • Generating Functional Test Cases with AI

Produces positive, negative, and boundary test cases using AI prompts. Students practise reviewing and refining AI output against standard test case quality criteria.

Lesson 2 • Organising and Maintaining AI-Generated Test Suites

Structures AI-generated test cases into maintainable suites aligned with project standards. Addresses the long-term quality of test assets produced with AI assistance.

Lesson 3 • Translating Requirements into Test Inputs

Converts requirement text into AI-ready prompts for test case generation. Directly applies prompt engineering skills from Chapter 2 to real test design tasks.

Lesson 1 • AI-Assisted Root Cause Analysis

Uses AI to hypothesise root causes from defect descriptions and logs. Teaches students to use AI as a reasoning partner, not a definitive authority.

Lesson 2 • Drafting Bug Reports with AI Assistance

Applies prompt engineering to transform raw observations into structured bug reports. Students practise editing AI drafts to meet team and project standards.

Lesson 3 • Defect Pattern Recognition Across Reports

Uses AI to identify recurring defect themes across a backlog of bug reports. Connects pattern insights to test strategy improvements introduced in later chapters.

Lesson 1 • Writing API and Integration Docs with AI

Uses AI to draft endpoint descriptions, parameter tables, and usage examples. Teaches students to verify technical accuracy against actual API behaviour.

Lesson 2 • Drafting Test Plans with AI

Generates structured test plan sections from project context and scope inputs. Students apply iterative prompting to produce plans that meet team review standards.

Lesson 3 • Maintaining and Updating Documentation

Applies AI to detect outdated content and generate updated sections after changes. Builds sustainable documentation habits that reduce documentation debt over time.

Lesson 1 • Measuring AI Impact on Testing Velocity

Defines metrics for quantifying AI contributions to testing speed and quality. Enables teams to make evidence-based decisions about AI tool adoption and investment.

Lesson 2 • Accelerating Regression Test Selection

Applies AI to prioritise regression tests based on code change impact. Reduces regression cycle time while maintaining coverage confidence in fast-moving sprints.

Lesson 3 • AI Support in Sprint Planning and Grooming

Uses AI to analyse user stories and surface testability issues before sprint start. Connects early AI involvement to reduced rework and clearer acceptance criteria.

Lesson 1 • AI Support for Performance Test Design

Applies AI to design load scenarios, identify performance risks, and interpret results. Connects AI-generated scenarios to realistic user behaviour models.

Lesson 2 • Handling AI Limitations in Complex Domains

Identifies where AI output quality degrades in specialised or safety-critical testing contexts. Builds professional judgment about when to rely on AI and when to override it.

Lesson 3 • AI-Augmented Security Test Planning

Uses AI to surface common vulnerability categories and generate security test ideas. Teaches students to treat AI security suggestions as starting points requiring expert validation.

Lesson 1 • Designing AI Governance for QA Artifacts

Establishes policies for reviewing, approving, and auditing AI-generated test and documentation artifacts. Ensures organisational accountability without blocking productivity gains.

Lesson 2 • Building a Prompt Library for Your Team

Creates a shared, versioned repository of validated prompts for common QA tasks. Reduces individual variation and accelerates onboarding of new team members.

Lesson 3 • Assessing AI Readiness in Your QA Team

Evaluates team skills, tooling, and process maturity before scaling AI adoption. Prevents failed rollouts by grounding strategy in honest capability assessment.

Lesson 4 • Communicating AI Strategy to Stakeholders

Prepares students to present AI adoption plans to leadership, developers, and product teams. Addresses common objections and frames AI as a quality enabler, not a headcount reducer.