The CT-GenAI syllabus consists of five major chapters:

Introduction to Generative AI for Software Testing

This chapter covers:

Foundations of Generative AI and LLMs
Capabilities and limitations in testing
Use cases across the test lifecycle
Human-AI collaboration models

You’ll learn how AI enhances productivity in test design, documentation, regression testing, and exploratory testing.

Prompt Engineering for Effective Software Testing

Prompt engineering is a core competency for AI-driven testing.

Topics include:

Writing structured prompts for test case generation
Controlling output format and quality
Reducing hallucinations
Iterative refinement strategies

Expect scenario-based exam questions where you must identify the best prompt structure for a testing objective.

Managing Risks of Generative AI in Software Testing

This section is highly practical and exam-relevant.

You’ll study:

AI hallucinations and output validation
Bias in generated test data
Data privacy and security risks
Regulatory and compliance considerations

Questions often test your ability to identify risk mitigation strategies rather than just definitions.

LLM-Powered Test Infrastructure

Modern AI testing goes beyond prompting.

This chapter introduces:

Retrieval-Augmented Generation (RAG)
LLMOps practices
AI agents in testing workflows
Integration with CI/CD pipelines

You may encounter architecture-based questions requiring you to select the correct infrastructure model for a given testing scenario.

Deploying and Integrating GenAI in Test Organizations

This strategic chapter focuses on:

AI adoption roadmaps
Governance and policies
Change management
ROI measurement
Ethical AI frameworks

Expect managerial and strategic scenario questions in this area.