Generative AI systems bring new power and new risk. Large language models and diffusion models can draft content, summarize knowledge, and drive conversations with users, but their outputs are probabilistic and often unpredictable. Testing them requires different thinking and different tools. The ISTQB Certified Tester Testing with Generative AI CT GenAI syllabus focuses on those differences so you can evaluate quality, safety, and business value with confidence.
This page gives you a practical, exam aligned overview of CT GenAI and a study plan you can follow. It also explains how CT GenAI differs from ISTQB Certified Tester AI CT AI so you choose the right course and the right exam. If you are looking for proxy exam help to pass CT – GenAI certification, we can help you. Contact us today.
CT GenAI at a Glance
Audience
Test Analysts, Technical Test Analysts, SDETs, QA Engineers, Product Owners, Data and ML Engineers, and anyone responsible for quality in teams that use or ship generative AI features
Level
Specialist level in the ISTQB scheme
Prerequisite
Foundation level knowledge in testing CTFL is strongly recommended. Some exam boards may require CTFL. Check your local board or training provider
Format
Scenario based multiple choice exam delivered by authorized providers. Time limits and language allowances vary by provider. Non native speakers may receive additional time
Goal
Give testers a toolkit to plan, design, run, and report tests for generative AI systems, including models integrated through APIs, prompt based features, and retrieval augmented generation RAG applications
What You Will Learn in CT GenAI
- Fundamentals of generative models such as large language models, diffusion models, and how they differ from traditional ML
- Risks unique to generative systems including hallucination, prompt injection, jailbreaks, privacy leakage, and toxic or biased content
- Practical evaluation methods for non deterministic outputs using rubrics, pairwise comparisons, win rate, and statistical testing
- Test design for prompts, system prompts, tools and function calling, and RAG grounding checks
- Data strategies for golden datasets, reference answers, and human in the loop review
- Safety testing including red teaming, guardrails, content filters, and policy compliance
- Observability and LLMOps for monitoring drift, model updates, prompt changes, and runtime behavior
- Reporting that explains coverage and residual risk in clear business terms
Syllabus Breakdown with Practical Examples
1 Generative AI Concepts and Risks
Understand the core ideas behind LLMs and other generative models. Learn why outputs vary across runs and why that affects test design and oracles. Analyze failure modes such as hallucination, overconfidence, sensitive data echo, and style drift. Explore common attack classes prompt injection, jailbreak attempts, and indirect prompt injection through external content. Know where prompt scope and system instructions fit into behavior.
Practical win
Create a short catalog of risks for your product context. For a customer service chatbot, list risks such as giving harmful advice, fabricating policy terms, or exposing confidential data. Turn each risk into a test idea.
2 Evaluation and Test Techniques for Generative Systems
Learn why classic deterministic oracles break down and how to replace them with scoring and comparison methods. Use rubric based scoring with clear criteria for factuality, relevance, completeness, helpfulness, and tone. Apply pairwise evaluation with human or model judges to compute win rates. Use metamorphic testing to check invariants such as consistency under paraphrase. Design adversarial inputs that probe the limits of safety and grounding.
Practical win
Write a two or three point rubric for a specific task like order status responses. Run a small A B test across two prompts and compute a simple win rate and confidence interval.
3 Prompts, Tools, and Retrieval Augmented Generation
Master the parts that control behavior. Structure system prompts so policy and persona are consistent. Test tool and function calling by validating input contracts and failure paths. For RAG systems, test the retrieval layer, grounding quality, and citation accuracy. Design evaluation sets that include grounded and ungrounded questions so you can measure the difference.
Practical win
Build a tiny set of grounded questions with known sources. Measure how often answers cite the right sources and how often they invent citations.
4 Data, Golden Sets, and Human in the Loop
Create high quality evaluation data. Define golden sets with reference answers or accepted ranges of answers. Use multi rater review to reduce bias and measure agreement with simple statistics. Manage privacy and security when using production logs to build evals. Keep evals versioned so you can see the impact of model and prompt changes over time.
Practical win
Draft a one page guideline for graders with do and do not examples. Track inter rater agreement on your next review round and tighten guidelines where agreement is low.
5 Safety, Ethics, and Compliance
Plan safety testing that matters. Cover toxicity, harassment, self harm content, hate speech, and protected attributes using clear policy definitions. Probe for PII leakage and training data echo. Include jurisdiction specific rules and industry standards where they apply. Use guardrails and filters, and then test those controls directly with adversarial inputs. Document escalation paths for safety incidents.
Practical win
Create a red teaming matrix that lists attack types, sample prompts, expected model behavior, and what artifacts to capture logs, screenshots, citations.
6 Non Functional Quality in Generative AI
Go beyond content correctness. Measure latency, cost per request, and throughput under realistic loads. Evaluate consistency under paraphrase or minor input noise. Check usability factors such as clarity of responses, error handling, and safe fallbacks. For multilingual contexts, test language switching and content filters across languages.
Practical win
Add simple checks to your pipeline that fail if median latency or cost per 100 calls exceeds budget.
7 LLMOps, Monitoring, and Change Management
Treat models and prompts as code. Version prompts and keep a change log. Monitor live behavior with sampling, automated checks, and human review. Detect drift in inputs and outputs. Run shadow or canary deployments for model upgrades and new prompts. Keep rollback plans ready.
Practical win
Create a one page release checklist for prompts and model changes with pre release eval pass criteria and a rollback trigger.
8 Tooling and Automation for CT GenAI
Choose and wire tools that support evaluation at speed. Use test harnesses that replay eval sets against models and store results. Enable pairwise comparisons and human review loops. Integrate with CI so updates to prompts or retrieval indexes run the right eval subsets automatically. Keep artifacts visible and explainable to non specialists.
Practical win
Add tags to each eval case so you can run only safety evals, or only RAG grounding cases, on demand.
Who Should Take CT GenAI
- Test Analysts and Technical Test Analysts who support teams building chatbots, content generation, and RAG features
- SDETs and QA Engineers who design automation and pipelines for AI powered applications
- Product Owners and Business Analysts who define acceptance criteria and risk for generative features
- Data and ML Engineers who want a testing perspective on LLM integration
Exam Snapshot and Smart Prep
The CT GenAI exam emphasizes practical judgment. You will see short scenarios about generative features and choose the most effective testing action or evaluation method.
Preparation ideas
- Build a tiny eval set for a real use case and run it through two prompt variants
- Write a short rubric for relevance and helpfulness and practice applying it
- Design three red team prompts that target your most important safety risks
- Add simple latency and cost checks to a mock pipeline and practice reporting results
Time and format vary by provider. Confirm details on the official ISTQB page for CT GenAI and with your exam provider.
A Four Week Study Plan That Works
Week 1
GenAI basics, risks, and oracles for non deterministic outputs. Create a small risk catalog for your product and draft three evaluation ideas for each risk
Week 2
Prompts, system prompts, and RAG. Build a tiny grounded eval set and measure citation accuracy. Practice pairwise comparison on two prompt styles
Week 3
Safety and non functional quality. Write a red teaming checklist, run two timed sessions, and summarize findings. Add basic latency and cost checks to your eval harness
Week 4
LLMOps and reporting. Version prompts, run a mock release with shadow traffic, and practice a one page status report that explains coverage and residual risk in business terms
Study for forty five to sixty minutes a day. Small and steady sessions beat cram weekends.
CT GenAI vs CT AI What is the Difference
Both certifications live in the AI Specialist stream but they focus on different kinds of systems and different testing problems. Use this guide to avoid buying the wrong course or exam.
| Topic | CT AI Certified Tester AI | CT GenAI Testing with Generative AI |
|---|---|---|
| Primary scope | Traditional ML systems classification, regression, clustering, computer vision | Generative systems large language models, diffusion models, RAG applications |
| Output type | Deterministic or probabilistic numeric labels, scores, classes | Open ended text, images, or code with stochastic variation across runs |
| Oracle challenge | Ground truth available for most cases through labeled datasets | No single correct answer in many tasks. Use rubrics, pairwise comparison, and human review |
| Test techniques | Data quality checks, feature engineering validation, model performance metrics like accuracy, ROC AUC, confusion matrix, explainability | Prompt and system prompt testing, metamorphic testing, red teaming, grounding and citation checks, toxicity and bias probes |
| Non functional focus | Model drift, fairness, explainability, robustness to data shift | Hallucination control, safety policy adherence, latency and cost tradeoffs, user experience and tone |
| Lifecycle and ops | MLOps for datasets, training pipelines, model deployment and monitoring | LLMOps for prompts, RAG indexes, tool calling, model selection, and runtime guardrails |
| Typical artifacts | Training data, features, trained model binaries, performance reports | Prompts, evaluation datasets, rubrics, RAG corpora and embeddings, safety policies, and judge configurations |
Simple rule of thumb
If your product predicts a value or a class, CT AI is the better fit. If your product generates content and you work with prompts, RAG, or tools, choose CT GenAI.
Frequently Asked Questions
Do I need to know how to train models
No. CT GenAI focuses on testing systems that use models. You do not need to build or fine tune models to pass the exam or to apply the skills at work
Is CTFL required
Foundation knowledge is strongly recommended and may be required by your local board. Check with your exam provider
Will I need to code
Some basic scripting or API knowledge helps when building evaluation harnesses, but the exam is scenario based and does not require writing code
Does CT GenAI cover images and audio
The main focus is on text generation and chat based systems. Many concepts carry over to other modalities, but confirm the current scope in the official syllabus
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