ISTQB CT-GenAI case studies
ISTQB CT-GenAI · Put your knowledge to work in context.
The ISTQB® Certified Tester – Testing with Generative AI (CT-GenAI) is a Specialist-level certification that extends the Foundation Level. According to ISTQB®, it "provides testing and quality-engineering professionals with a structured, hands-on path to master how, when, and why to use Large Language Models (LLMs) and other generative-AI techniques across the entire test process – from requirements analysis and test design to automation, reporting, and continuous improvement." It is aimed at testers, test analysts, test engineers, automation engineers and quality-engineering practitioners who already hold foundation-level testing knowledge and now want to apply generative AI responsibly and effectively in daily test work. The ISTQB CT-GenAI case studies are 5 cases and about 2,910 words.
1Pilot of an LLM for Regulatory Acceptance Test Drafts
ClaimsCo, a health insurer, is piloting a vendor-hosted LLM to draft acceptance tests for a claims portal with 1,200 regulatory requirements. The release is in three weeks.
608 words, about 3 min2AI-Generated Test Conditions for Partial Refunds
NorthCart, an online retailer, is rebuilding its returns portal. The business requirement says customers may return items within 30 days for a full or partial refund; tax must be recalculated; original shipping fees are non-refundable; gift cards are refunded as store credit.
621 words, about 3 min3LLM-Generated API Tests in a Payments Migration
PayBridge is migrating 24 REST endpoints from a monolith to microservices. A test engineer with one year of automation experience asks an LLM to generate a pytest suite from the OpenAPI 3.0 specification.
570 words, about 3 min4LLM Failure Summary in a CI Dashboard
SaaSly runs 600 UI and API tests nightly. The CI dashboard sends failed logs to an LLM that produces a one-paragraph summary and a severity label for the daily triage call.
544 words, about 3 min5Synthetic Test Data for a Clinical Scheduling App
MedSchedule is building a clinical appointment scheduler. The test team needs 5,000 patient records for performance and workflow testing but cannot use production data.
567 words, about 3 min