That question breaks every test you know how to write. This course teaches you to answer it the way AI teams need: a pass rate, a confidence interval, a threshold the product owner signed, and the traces of the ones that failed. In 23 weeks, from your first terminal command to a release assessment you present on video.
Every team is putting a language model in front of customers, and every one of them is discovering that "run the test case and compare" does not apply. They need someone who can design an oracle for an answer that is never the same twice. That someone is a tester who has learned six new things, not a data scientist who has learned testing.
Design cases from requirements. Hunt edge cases. Explore. Write defect reports a developer can act on. Argue about severity. Decide what "done" means.
Exactly that, applied to a system whose output varies: golden datasets, rubrics, judges calibrated against humans, pass rates with intervals, retrieval and agent testing, red-teaming, and a gate in CI.
Enough Python to run the tools. Enough statistics to know what twenty runs can and cannot tell you. The vocabulary. And a portfolio that proves it. That is the whole course.
Every module ends with something in your GitHub repository that a hiring manager can open, run, and read. These are the ones interviews are built around.
Ten Playwright tests with page objects against a live demo shop, twenty consecutive green runs, traces uploaded on failure, and three product defects documented from the evidence.
One support-bot prompt, twenty runs, five property checks, a generated summary table, and a page of analysis proposing thresholds a product owner could sign.
A 40-row golden set, deterministic checks, a rubric judge calibrated against human grading, per-category thresholds with confidence intervals, and a pull-request gate plus a nightly drift run.
Retrieval measured with no model at all, grounding checked claim by claim, scripted conversations, and trajectory tests that catch a destructive tool call before it happens.
A threat model, a 30-case adversarial set across the OWASP LLM Top 10, an automated sweep, an hour of manual depth, and findings with severities, rates, and retest results.
The capstone: strategy, full suite in CI, red-team, a two-page executive summary with a go or no-go, and a ten-minute recorded walkthrough of your own work.
Harbour View Hotel has a support bot that answers policy questions from a document, and a concierge agent that can look up, check, and cancel bookings. Both are built in the course from parts you write yourself, so you understand every line. Both have defects planted in them.
Six in the bot. Four in the agent. Two more in the release candidate you assess in the capstone. You get the code. You do not get the list. Your evaluation suite has to find them, and the reveal at the end shows your pass rates moving as each one is switched off.
Nothing is simulated with slides. The bot calls a real model. The agent's cancel tool really deletes, on a sandbox API built for testing, on bookings you created.
Stage A has no AI in it on purpose: seven weeks of foundations so that every tool in Stage C is something you can run and read. Learners who already write Python skip Modules 2 and 3 and finish in about nineteen weeks.
Every module has a full page of lesson content: objectives, teaching notes, student material, checkpoints with answer keys, and labs with a definition of done. Read the complete syllabus.
Every lesson has the same five parts, so you always know what done looks like. Tools change every six months; the course teaches the principle first and the tool second, and the tool lessons are re-recorded when they break.
Two to four outcomes you can be tested on.
Real, runnable code in every lesson, verified before it ships to you.
Three questions and an answer key, every lesson.
A definition of done and points, not "looks good".
Every model call prints its tokens and its price. The whole course costs under ten dollars in API credit.
Chandru builds and tests software products, and wrote every lesson, lab, and line of practice code in Zero to AI Tester. The course is the path Chandru wanted and could not find: one that starts at the terminal and ends at a release assessment.
The practice product in this course, a hotel support bot and a booking agent with twelve planted defects, was built and broken deliberately so that learners find real bugs with real evaluation suites instead of watching slides. The first eval run against it fails three of five categories and one safety check, and finding out why is the job the course trains you for.
Built for testers who are told AI will replace them, by someone who thinks the opposite: the people who already know how to design a test are the people AI teams need most.
The content is the same in every tier. The difference is how much of a person you get with it.
Fourteen-day refund on any tier, no questions, for anyone who has not yet started Module 3. The API credit you buy from the model provider is separate and yours; the entire course uses under ten dollars of it.
Yes, and the course is built for exactly that. Module 2 is three weeks of Python from the first line, with every example about testing. It is the hardest part and the place people quit, so it is paced to be finishable, and every lesson ends with something that runs. You will be reading and writing test code by week four and driving a browser by week six.
About seven hours a week for twenty-three weeks: two weekday evenings of lessons and one weekend block for the lab. First-time coders usually spend a third longer on the labs in Modules 2 to 4, and the pace allows for that. If you already know Python, skip Modules 2 and 3 and finish in about nineteen weeks.
An API key from the model provider with a small prepaid credit. The whole course, including the evaluation labs and the capstone, runs on under ten dollars of it, and every script prints its cost so there are no surprises. Everything else is free and open source: Python, pytest, Playwright, promptfoo, DeepEval, Langfuse's free tier.
Python, pytest, requests, Playwright, GitHub Actions, the Claude API, a local embedding model, promptfoo, DeepEval and RAGAS, Langfuse, Garak and PyRIT as a survey, Great Expectations, and AI coding assistants including Claude Code with the Playwright MCP server. More importantly, the principles behind each, because the tools will change and the principles will not.
You get a certificate of completion for the cohort tier, and something better in every tier: a public portfolio of seven projects with a recorded release assessment. Module 10 maps the course onto the ISTQB CT-AI syllabus so you can sit that exam with a day of extra reading if your target employers filter on it.
No. Everything in Module 9 is run against the practice app on your own machine and a sandbox API built for testing. The module opens with the authorisation rule and the course terms require it. You learn the method on a system you own, which is how real engagements work too.
The fifteen-minute rule and a "what I ran, what I expected, what happened, what I tried" template, taught in Module 0. Then the community forum in every tier, and the weekly live lab in the cohort tier. Nobody is left alone with a traceback.
That sentence is the job. This is the course that gets you to it.