Generative AI is no longer a futuristic concept; it’s a core component of modern software, powering everything from customer service chatbots to developer copilots. But this new technology breaks traditional testing paradigms. How do you test a system that is non-deterministic by design? How do you validate for “helpfulness” or guard against “hallucinations”?
The ISTQB® Certified Tester – Testing with Generative AI (CT-GenAI) is a foundation-level extension certification designed to answer these questions. It provides a structured, comprehensive framework for quality assurance professionals tasked with validating the complex, unpredictable, and powerful world of generative AI applications.
What is the ISTQB CT-GenAI Certification?
The CT-GenAI certification is a specialist credential from ISTQB® that focuses exclusively on the challenges of testing systems built with generative AI. This includes applications using Large Language Models (LLMs), diffusion models for image generation, and complex Retrieval-Augmented Generation (RAG) pipelines.
The syllabus provides the essential vocabulary, techniques, and strategies needed to effectively plan, design, and execute tests for systems with non-deterministic outputs. You will learn to manage critical risks like safety and compliance, evaluate the quality of AI-generated content (assessing factuality, style, bias, and toxicity), and integrate robust guardrails into your team’s delivery pipelines. If you work with chatbots, copilots, content generators, or RAG-based assistants, the CT-GenAI certification formalizes the critical skills required for success.
Who is the CT-GenAI Certification For?
This certification is ideal for a wide range of professionals who are responsible for the quality and risk management of AI-driven products. You should consider this certification if you are a:
- Functional and Non-Functional Tester transitioning to test AI-enabled products.
- SDET or Automation Engineer tasked with building LLM-aware tests and evaluation frameworks.
- Test Manager, QA Lead, or Product Owner accountable for the overall quality and safety of AI features.
- Data Engineer, ML Engineer, or MLOps Practitioner looking to establish a strong baseline in AI testing principles.
- Prompt Engineer seeking to formalize your evaluation and red-teaming skills.
- Risk, Compliance, or Safety Stakeholder who needs to understand and assess the behavior of AI models.
What are the Key Learning Objectives for CT-GenAI?
Upon successfully completing the certification, you will have the skills to:
- Explain core GenAI architectures relevant to testing, including LLMs, embeddings, RAG, and agentic tool-use.
- Plan comprehensive test strategies that account for stochastic, prompt-conditioned behavior.
- Design effective evaluations for key quality attributes like accuracy, consistency, robustness, harmlessness, and helpfulness.
- Measure and mitigate hallucinations by applying grounded evaluation techniques with reference corpora.
- Test critical guardrails, including content filters, policy enforcement, prompt hardening, and jail-break resistance.
- Execute A/B, offline, and online evaluations while correctly analyzing sampling variance.
- Incorporate data governance principles covering PII handling, IP/copyright issues, user consent, and data provenance.
- Report risk-based findings effectively to drive go/no-go decisions for new AI features.
A Glance at the CT-GenAI Syllabus
The official syllabus is structured into seven key areas, providing a complete overview of the generative AI testing landscape.
- Chapter 1: GenAI Foundations for Testers Covers LLMs, tokenization, temperature & sampling, embeddings, vector search, RAG pipelines, and agent/tool-use orchestration.
- Chapter 2: Quality Attributes for GenAI Defines critical quality characteristics like factuality, coherence, style adherence, bias, toxicity, safety, privacy, IP compliance, latency, and cost efficiency.
- Chapter 3: Test Design for Non-Determinism Explores techniques like prompt/seed control, scenario coverage, perturbation testing, adversarial prompts, red-teaming, and metamorphic testing.
- Chapter 4: Evaluation Methods and Metrics Details human-in-the-loop reviews, graded rubrics, reference-based scoring (BLEU/ROUGE), pairwise preference, and LLM-assisted evaluation.
- Chapter 5: Data and Environment Management Focuses on managing golden sets, reference answers, synthetic data generation, dataset versioning, privacy controls, and ensuring reproducibility.
- Chapter 6: Automation & CI/CD Integrates testing into the development lifecycle with LLM evaluation harnesses, prompts-as-code, drift monitors, shadow deployments, and feedback loops.
- Chapter 7: Risk, Ethics, and Governance Addresses policy alignment, model cards, incident response, explainability limitations, and documentation for audits and approvals.
Download CT-GenAI syllabus
The CT-GenAI Exam Structure
- Format: 40 multiple-choice questions.
- Duration: 60 minutes. An additional 15 minutes (25% extension) is granted for candidates taking the exam in a non-native language.
- Passing Score: Approximately 65% (26 out of 40). You must confirm the exact score with your local ISTQB® member board or exam provider.
- Level: Foundation Extension.
- Question Types: The exam includes single-select and scenario-based questions mapped to K1 (Remember), K2 (Understand), and K3 (Apply) learning objectives.
Sample Exam Question Themes
- Scenario: Given a RAG chatbot design, identify the most likely points of failure or risk.
- Choice: Choose the most appropriate evaluation method to test for hallucinations versus testing for harmlessness.
- Application: Select the best test data strategy to probe a model for potential bias or toxicity.
- Prioritization: Given compute and cost constraints, prioritize which tests should be executed first.
What are the Prerequisites?
A prerequisite for this exam is the ISTQB® Certified Tester Foundation Level (CTFL) certification. Additionally, a basic familiarity with APIs, JSON, HTTP, and CI/CD tools is highly recommended. While not required, hands-on exposure to a GenAI system like a chatbot, code assistant, or image generator is helpful.
CT-GenAI vs. CT-AI: What’s the Difference?
While both certifications are in the AI testing space, they have distinct focuses.
The CT-AI certification covers the broader AI/ML landscape. It focuses on testing classical machine learning models, data pipelines, feature engineering, and the ethical considerations for predictive systems. The emphasis is on validating training data, model performance metrics like precision and recall, and managing the lifecycle of deterministic software that uses statistical models.
The CT-GenAI certification zooms in specifically on generative systems like LLMs and diffusion models. Here, the challenge is testing open-ended, non-deterministic outputs. The focus shifts to prompt-conditioned behavior, hallucinations, safety, tone, policy guardrails, and specialized evaluation harnesses (both offline and online).
In short: CT-AI is broad AI testing; CT-GenAI is a deep dive into generative behavior and its unique guardrails.
Frequently Asked Questions (FAQ)
Is programming experience required for the CT-GenAI exam?
No. While scripting skills are beneficial for implementing these tests in practice, the exam itself evaluates your testing knowledge, not your coding fluency.
Will I need to know specific vendor tools?
No. The certification is vendor-neutral. You should understand the categories of tools (e.g., vector databases, evaluation harnesses) and their capabilities, not proprietary commands for a specific product.
Does the exam involve complex mathematics?
No. You may encounter conceptual questions related to basic statistics (e.g., the purpose of sampling, variance, or confidence levels), but you will not be required to perform heavy mathematical calculations.
How much do I need to know about ethics and legal topics?
The exam covers practical, test-focused aspects of ethics and governance. You should understand concepts like policy alignment, privacy (PII), intellectual property (IP), and safety frameworks from a tester’s perspective.
How is non-determinism tested on a multiple-choice exam?
The questions will focus on your ability to select the right strategies. For example, you might be asked to choose a method to control variance (like using fixed seeds or structured prompts) or to design a reliable evaluation process that accounts for randomness.
How to Prepare for the Exam
- Review the official CT-GenAI syllabus and glossary from ISTQB® or your local board.
- Build a small “golden set” of questions and reference answers relevant to a domain you know.
- Practice crafting adversarial prompts and prompt variants against a public model to see how its behavior changes.
- If you need exam help service for CT-GenAI, contact us directly.
- Join a study group to discuss concepts and challenge each other’s understanding.
Online Exam Availability
Currently (as of 10th May 2026) all exam boards and exam providers are offering remote exam on CT-GenAI. To register with ISQI, follow the link below –
https://isqi.org/ISTQB-Certified-Tester-Testing-with-Generative-AI-CT-GenAI/CT-GenAI.14