Course

GenAI Testing
for QA engineers and automation testers

Testing a GenAI application is not testing a form. The same input can give a different answer twice, "correct" is a judgement rather than an assertion, and the failure you most need to catch — a confident, well-written, entirely false answer — passes every check a traditional suite makes. This course is about the testing techniques that do catch it.

Start Module 1 free See plans from ₹499

Who this course is for

Manual testers who want to stay employable as their product grows an AI feature, automation testers and SDETs who need to write assertions against a model's output, and QA leads who have been handed an LLM feature and asked for a test plan. You need working knowledge of software testing. You do not need a machine-learning background, and Python is taught from zero in the companion Python and DSA track, which the same subscription covers.

What the 16 modules cover

Foundations and evaluation

What a large language model actually does, why the same prompt gives different answers, and how to score an answer you cannot diff. Prompt engineering from a tester's point of view, and building a ground-truth set you can measure against.

Red-team and safety testing

Hallucination detection, prompt injection and jailbreaks, data leakage, bias and toxicity. Writing adversarial cases on purpose, and deciding what counts as a defect rather than a model being a model.

RAG systems

Retrieval quality, chunking and embedding choices, citation faithfulness, and knowledge-base poisoning — where a single planted document changes what the system tells every user.

Agents, multi-agent systems and MCP

Testing a system that decides its own next step: tool selection, refusals, budget and loop limits, hand-offs between agents, and Model Context Protocol tool calls.

Automation

Promptfoo, DeepEval and RAGAS wired into a suite that runs in CI, plus observability and tracing so a failure in production is diagnosable rather than just reported.

Two capstone projects

Not a walkthrough video — a running application you attack. See the projects section below.

All 16 modules, in order

The whole syllabus is here to read before you decide anything — no sign-up, no email, no "request the curriculum". Module 1 is free to work through in full; the rest need a subscription. About 93 hours of material in total.

Module 1 Introduction to Gen AI What generative AI is, how it differs from traditional software, and why it needs a different testing mindset. Free — start now3h
Module 2 Basics of LLM How large language models actually work — tokens, context, parameters — and the failure modes that follow from the architecture. Subscribers4h
Module 3 Prompt Engineering Designing, versioning and testing prompts as production artefacts rather than throwaway text. Subscribers4h
Module 4 Red Team Testing Adversarial testing of GenAI applications — deliberately making a system misbehave, and documenting it responsibly. Subscribers5h
Module 5 Capstone Project — Requirements PRD, Test Plan, Test Cases & Reports The complete manual-testing documentation set for a GenAI application, produced end to end. Subscribers6h
Module 6 Promptfoo — Evaluation Automating prompt and model evaluation with Promptfoo: declarative test cases, assertions and CI integration. Subscribers6h
Module 7 Promptfoo — Red Team Testing Using Promptfoo's red-team tooling to generate and run adversarial suites automatically. Subscribers6h
Module 8 Python for Testers The Python a tester actually needs to drive an evaluation framework — no computer-science detour. Subscribers5h
Module 9 Pytest Turning GenAI evaluations into a real test suite with pytest — fixtures, parametrisation and CI reporting. Subscribers5h
Module 10 RAG Development Building a retrieval-augmented generation pipeline so you understand every seam where it can fail. Subscribers6h
Module 11 MCP with Multi-Agent Development Model Context Protocol and multi-agent systems — how agents get tools, and how coordination fails. Subscribers7h
Module 12 RAGAS Metrics for RAG Measuring RAG quality objectively with RAGAS — faithfulness, relevancy and the retrieval metrics. Subscribers5h
Module 13 DeepEval for Agents Unit-test style evaluation of LLM and agent behaviour with DeepEval, integrated into pytest. Subscribers5h
Module 14 Observability with LangSmith Tracing, monitoring and debugging LLM and agent applications in development and production. Subscribers4h
Module 15 Capstone Project #1 — RAG-based Build, test and report on a complete RAG application, applying everything from modules 1–14. Subscribers10h
Module 16 Capstone Project #2 — MCP with Multi-Agents Build, test and report on a multi-agent system over MCP, the hardest thing in the course. Subscribers12h

Start Module 1 free Unlock the rest from ₹499

You test real running applications, not slides

Three applications are hosted for you, and you work them from a browser with no installation. This is the part that separates the course from a video playlist: you are looking at a real system's actual output when you decide whether it is a defect.

Project 1 — the RAG application

A travel-planning assistant in five versions, each one broken in a different, deliberate way. You run the retrieval, inspect the vector store, plant a poisoned document and watch it change the answers, then run red- and blue-team suites against it and read the trace of everything you did.

Project 2 — the multi-agent application

A booking system built from several agents with real tool calls over MCP. You test hand-offs, watch an agent refuse a call it is not allowed to make, break the budget check, and tell the difference between a control working and a component failing.

Certification

Three separate exams — Basic, Advanced and Expert. Each one you pass earns its own certificate carrying a unique number, and anyone can check that number against this site without an account, which is what makes it worth putting on a CV. Nothing is awarded for finishing the videos; the exams are the bar.

What it costs

Module 1 is free once you have an account — no card, nothing else to set up. After that one subscription unlocks the rest of this course and the whole Python and DSA track: ₹499 for a month, ₹1,199 for three months, ₹3,999 for a year. Nothing renews on its own, and buying again while you still have time left adds days rather than replacing them. Full pricing.

Common questions

How this compares with ISTQB CT-AI, whether you need Python first, and what an AI testing career actually looks like are all answered on the FAQ page.