AI and GenAI testing
questions, answered straight

Yes, and the distinction matters. Most Generative AI courses teach you to build with a model — prompt it, chain it, ship a feature. A testing course starts from the opposite question: this thing already exists, how do I decide whether it is broken? That means evaluation instead of generation, adversarial cases instead of happy paths, and a definition of "correct" you can defend in a bug report. This course is written for manual testers, automation testers and SDETs, and assumes no machine-learning background.

It depends on what you are missing, and it is worth being honest about the three different things on offer. If you need a recognised line on your CV, ISTQB CT-AI is the vendor-neutral certification hiring managers recognise, though it is an exam syllabus rather than hands-on practice. If you want breadth cheaply, the large marketplaces (Udemy, Coursera) have inexpensive video courses, but you are mostly watching someone else test. If what you lack is practice — actually deciding whether a real model's real output is a defect — then what you need is a course with a running application you can attack, because that judgement is the skill that does not transfer from video. Pick for the gap you actually have, and there is no reason not to combine a certification exam with a hands-on course.

Test the two halves separately before you test the whole, because a RAG system has two independent failure modes that look identical from the outside. First retrieval: for a set of questions with known-correct source documents, did the right chunks come back, and in what rank? That is measurable with ordinary precision and recall — no model judgement needed. Then generation: given the chunks that were retrieved, is the answer actually supported by them? This is faithfulness or groundedness, and it is where you check every claim in the answer against the cited text. The reason to split them is diagnostic: a wrong answer from good retrieval is a generation problem you fix with prompting, while a wrong answer from bad retrieval is a chunking, embedding or indexing problem, and prompt changes will never fix it. Then test the cases only a full system has: no relevant document exists (does it say so, or invent something?), contradictory sources, and a deliberately poisoned document in the knowledge base.

There is no single check, so use layers. The strongest and cheapest is grounding: if the system is supposed to answer from provided documents, verify every factual claim in the output appears in those documents, and treat anything unsupported as a hallucination regardless of how plausible it reads. Where there is no source text, self-consistency is the practical substitute — ask the same question several times at non-zero temperature, and answers that change on facts that should not change are a signal, because a model's fabrications are far less stable than the things it actually knows. Third, an LLM-as-judge check scores the answer against a reference, which scales well but inherits the judge model's own blind spots, so it needs spot-checking by a human rather than being trusted outright. Finally, plant questions whose true answer is "I don't know" — questions about entities that do not exist. A system that confidently answers those will confidently answer anything, and that single test finds more real problems than any score.

Attack the four surfaces separately. Prompt injection in the user's input: instructions telling the model to ignore its own rules. Then indirect injection, which is the one teams miss — the malicious instruction lives inside a document the system retrieves, so the attacker never talks to your application at all and nothing in your input validation ever sees it. Then data leakage: can you get the system to reveal its system prompt, another user's content, or a document you should not have access to? Then knowledge-base poisoning: add one plausible but false document and see how many users' answers change. Write these as ordinary test cases with expected results, and note that a refusal is a pass, not a failure — a lot of red-team reports are wrong because the tester recorded a working control as a bug.

The realistic order is: keep the testing judgement you already have, then add the three things that are missing. First enough Python to write an assertion — you do not need to be a developer, but every evaluation tool in this space is a Python library. Second evaluation technique: how to score an output that has no single right answer, how to build a ground-truth set, and what precision, recall and faithfulness mean in practice. Third the specific failure modes of these systems: hallucination, injection, retrieval failure, and agents that loop or call the wrong tool. Your existing instinct for where software breaks is the part that takes years and you already have it; the AI-specific layer on top is a matter of months, not years. Build something you can show — a small evaluation suite against a real application is worth more in an interview than any certificate on its own.

Manual testing as "execute this documented script by hand" has been shrinking for years and AI has accelerated that, because generating and running routine checks is exactly what these tools are good at. But the judgement underneath manual testing — deciding what is worth testing, recognising that an output is wrong in a way no specification anticipated, knowing which bug actually matters — has become more valuable, not less, precisely because AI systems fail in ways that require a human to notice. The honest answer is that the job title is at risk and the skill is not. Testers who add evaluation technique and enough coding to automate their own checks are in demand; testers who only run scripts by hand are competing with software that does it for free.

For vendor-neutral recognition, ISTQB's Certified Tester AI Testing (CT-AI) is the one most widely recognised by employers and the one to name if a job description asks for a certification. Cloud providers also offer AI certifications, but those are oriented to building and deploying on their own platform rather than testing. Certificates from a specific course, including the three levels awarded here, are evidence you did the work rather than an industry credential, which is why the number on ours is publicly checkable — it is worth exactly as much as the work behind it, and no more. In practice, most people who get hired have both a recognised certification and something they built that they can talk through.

Several institutes in Hyderabad run classroom AI testing courses, and it is worth asking any of them one question before paying: do you test a real running AI application during the course, or only watch demonstrations? That single question separates the useful ones from the rest. This course is run from Hyderabad and is delivered entirely online, so it is the same material whether you are in the city or not — you work three hosted applications from your browser, at your own pace, with nothing to install. Pricing is in rupees and Phase 1 is free, so you can see the material before deciding.

Not to start, but you will need some. The first modules are conceptual and hands-on through a browser interface, so you can begin with no code at all. By the time you reach automation, every tool in this space — Promptfoo, DeepEval, RAGAS — is driven from Python, and you will need to read and write functions, loops and assertions. You do not need object-oriented design, algorithms or a computer science degree. That is why the Python and DSA track is included in the same subscription rather than sold separately: the amount of Python a tester needs is roughly the first two phases of it.

Yes, and it is a real free tier rather than a trailer. Module 1 of the GenAI Testing course and the whole of Phase 1 of the Python and DSA course (modules 1 to 4) are free once you create an account — no card, nothing to cancel. Everything after that needs a subscription: ₹499 for a month, ₹1,199 for three months or ₹3,999 for a year, and one subscription covers both courses. Nothing renews automatically, and buying again while you still have time left adds days rather than replacing them.

Yes. Each certificate carries a unique number, and anyone — an employer, a recruiter, anyone you send the link to — can type that number into the verification page and see whether it is real, without an account and without contacting us. This matters because an image file proves nothing; a number someone else can independently check is the only form of certificate that carries weight.

If you are testing AI systems, learn an evaluation framework before anything else — Promptfoo is the easiest starting point because its test cases are readable YAML, while DeepEval and RAGAS go deeper on RAG-specific metrics like faithfulness and context precision. If instead you mean using AI to help with ordinary testing work, the highest-value habit is using a coding assistant to draft and maintain your test code, with the discipline of reviewing every line it produces — an unreviewed generated test that passes for the wrong reason is worse than no test, because it reports safety you do not have. Learn the evaluation framework first either way: it is the skill that is specific to this work and hardest to pick up on the job.