From traditional QA to
AI Quality Engineering

No. The course starts with the fundamentals of modern AI and gradually moves into applied AI Quality Engineering. You will learn the core concepts needed to understand how AI systems work, including prompts, tokens, embeddings, context windows, RAG, hallucinations, LLMs, and AI agents.

QA experience is helpful because many ideas build on testing, validation, test cases, regression, risk analysis, and defect investigation. However, machine learning, data science, or advanced math experience is not required.

The cohort runs for 24 weeks starting on Tuesday, June 2, 2026 at 7:30 PM. Calculating countdown...

The program is designed as a structured learning path, not a short workshop. The goal is to give students enough time to understand AI fundamentals, practice AI-QE testing methods, complete assignments, review interview questions, and build confidence with real AI testing scenarios.

The course is delivered live online with two 2.5-hour sessions each week. Classes combine explanation, instructor-led demos, hands-on exercises, quizzes, homework, review sessions, and interview-focused discussion.

The live format allows students to ask questions, review difficult topics, discuss AI failures, and practice explaining AI-QE concepts in a clear professional way.

Traditional software usually has deterministic behavior: the same input should produce the same output. AI systems are different. The same prompt may produce different responses, and an answer can sound correct while still being incomplete, unsupported, unsafe, or factually wrong.

AI-QE focuses on testing correctness, groundedness, hallucination risk, retrieval quality, prompt behavior, tool usage, safety, output format, and consistency across repeated runs. Instead of only asking “did it work?”, AI-QE asks whether the answer is reliable, supported by evidence, useful, safe, and appropriate for the user’s request.

Yes. The course focuses on realistic AI application patterns that QA engineers are likely to see in modern companies. These include chatbots, RAG systems, AI agents, structured outputs, prompt-driven workflows, document assistants, search assistants, support bots, and tool-using AI systems.

Students learn how to test both the final AI response and the system behavior behind it, including retrieved context, prompt instructions, tool calls, output formatting, and failure handling.

Yes. The course includes AI-QE vocabulary, practical testing examples, scenario-based discussion, and 100+ AI QA interview questions and answers. Students will practice explaining AI concepts, describing failure modes, designing AI test strategies, and answering questions about prompts, RAG, hallucinations, agents, evaluation, and safety.

The goal is not only to memorize answers, but to speak like someone who understands how AI systems fail and how quality engineers should test them.

This course is designed for QA engineers, software testers, SDETs, automation engineers, manual testers, test leads, and software engineers who want to understand how to test AI-powered applications.

It is especially useful for QA professionals who want to move from traditional web, mobile, API, or automation testing into AI testing, AI-QE, AI product validation, or AI quality roles.

The most important topics are golden dataset testing, hallucination detection, factuality checks, RAG evaluation, prompt testing, prompt injection testing, LLM-as-a-judge evaluation, AI agent tool-use testing, structured output validation, safety testing, and regression testing for AI systems.

These topics are emphasized because they represent the most common and highest-risk failure areas in real AI applications.

A golden dataset is a carefully prepared set of test questions, expected answers, expected concepts, edge cases, and negative cases used to evaluate AI behavior. It gives AI-QE teams a repeatable way to measure whether the system is improving or getting worse over time.

In traditional QA, you may compare actual results to expected results. In AI-QE, the expected result may be more flexible, so golden datasets often include rubrics, required concepts, acceptable variations, and examples of unacceptable answers.

Hallucination testing checks whether an AI system invents facts, citations, policies, numbers, explanations, or details that are not supported by the source material or known truth.

Students learn how to test questions with known answers, questions with missing information, source-based questions, and cases where the correct behavior is for the AI to say that it does not know. This is one of the most important areas of AI-QE because hallucinations can look confident and professional while still being wrong.

RAG stands for Retrieval-Augmented Generation. It is a common AI architecture where the system retrieves relevant documents or chunks and then uses an LLM to generate an answer based on that context.

RAG evaluation checks two major areas: whether the system retrieved the right context, and whether the model used that context correctly. Students learn how to test chunking, embeddings, vector search, top-k retrieval, metadata filtering, source coverage, groundedness, and citation accuracy.

A retrieval failure happens when the system does not find the right information. For example, the answer exists in the knowledge base, but the RAG system retrieves the wrong document or misses the most relevant chunk.

A generation failure happens when the system retrieves the correct information but the model still produces a wrong, incomplete, unsupported, or poorly formatted answer. AI-QE must separate these two failure types because they require different fixes.

The course covers both, but the main focus is prompt testing. Prompt engineering is about designing better prompts. Prompt testing is about verifying whether prompts behave correctly across normal cases, edge cases, ambiguous requests, repeated runs, malicious inputs, and production-like scenarios.

Students will learn how to test instruction following, output format, tone, refusal behavior, prompt drift, prompt injection resistance, and consistency.

Prompt injection testing checks whether an AI system can be manipulated by malicious or conflicting instructions. For example, a document may contain text such as “ignore previous instructions,” or a user may try to force the system to reveal hidden instructions or bypass safety rules.

Students learn how to design adversarial test cases and verify that the AI follows the correct instruction hierarchy instead of obeying unsafe or irrelevant instructions.

Yes. AI agents are systems where the model can use tools, APIs, calculators, databases, browsers, files, or other external functions to complete a task. Testing agents requires more than checking the final answer.

Students learn how to test whether the agent selects the correct tool, passes the right parameters, handles tool errors, respects permissions, avoids unnecessary tool calls, and correctly interprets tool results before producing the final answer.

Yes. LLM-as-a-judge means using another AI model to evaluate outputs based on a rubric. This can help scale evaluation when manual review is too slow.

Students will learn how to design judge prompts, define scoring dimensions, evaluate correctness, completeness, groundedness, safety, clarity, and format compliance, and understand the limitations of automated judging. The course also explains why judge results should be validated and not trusted blindly.

Some examples may include simple scripts, structured test cases, JSON outputs, evaluation prompts, and practical demos. The course is designed for QA engineers, so the technical level is practical rather than research-heavy.

Students do not need to be advanced programmers, but basic comfort with QA concepts, APIs, JSON, logs, and simple automation will be helpful.

Python is helpful but not strictly required at the beginning. The course can explain examples step by step. However, students who want to go deeper into automation, evaluation scripts, RAG demos, and AI test pipelines will benefit from basic Python knowledge.

The course is designed so that QA engineers can understand the testing logic even when the demo includes code.

The course may discuss common AI and AI-QE tools such as OpenAI or Anthropic APIs, LangChain, LlamaIndex, vector databases, evaluation frameworks, observability tools, JSON validators, API testing tools, and local model tools.

The goal is not to memorize one specific tool. The goal is to understand the testing concepts behind AI systems so students can apply them across different platforms.

Yes. The course uses modern LLM behavior as a foundation for understanding AI-QE. Students will learn how to evaluate LLM outputs, compare responses, test prompt behavior, analyze hallucinations, and reason about non-deterministic answers.

The concepts apply broadly to LLM-based systems, not only to one specific model or vendor.

Yes. Structured output testing is an important part of production AI-QE. Many AI systems send model outputs to another program, parser, API, or workflow. In those cases, even a small formatting issue can break the application.

Students learn how to test JSON validity, schema compliance, required fields, no-extra-field rules, markdown formatting, language requirements, length limits, and parser compatibility.

Yes. The course covers safety, bias, fairness, harmful output, privacy risks, data leakage, over-refusal, under-refusal, and adversarial testing.

AI safety testing is not only about blocking bad outputs. A good AI-QE strategy checks whether the system gives the safest useful answer for the situation while still respecting product requirements and user intent.

AI regression testing checks whether changes to prompts, models, RAG pipelines, embeddings, chunking, tools, or safety rules break behavior that used to work.

This is critical because improving one AI behavior can accidentally make another behavior worse. Students learn how to use test datasets, repeated runs, metrics, and comparison methods to detect quality regressions before release.

Traditional bugs are often deterministic: a button does not work, an API returns the wrong status code, or a calculation is incorrect. AI bugs can be probabilistic, contextual, and harder to reproduce.

An AI system may answer correctly nine times and fail on the tenth. It may fail only with certain wording, missing context, bad retrieval, high temperature, long prompts, or conflicting instructions. AI-QE teaches students how to classify and investigate these failure patterns.

The course is designed to give students practical examples they can discuss in interviews or use as the foundation for portfolio work. These may include AI test cases, evaluation rubrics, hallucination tests, RAG test scenarios, prompt regression examples, and agent tool-use testing examples.

A strong AI-QE portfolio should show not only that you used AI tools, but that you understand how to evaluate and debug AI behavior systematically.

The course includes theory only where it helps students test systems better. The main focus is practical AI-QE: how to design test cases, identify failure modes, evaluate AI outputs, debug RAG issues, test prompts, validate agent behavior, and explain risks.

Students will learn concepts, but the course is built around applied testing judgment.

After completing the course, students should be able to explain how modern AI systems work, design AI-QE test cases, evaluate prompt behavior, test RAG systems, detect hallucinations, validate structured outputs, test AI agents, create evaluation rubrics, and discuss AI testing confidently in interviews.

The course does not promise employment, but it is designed to give QA professionals the knowledge and language needed to move toward AI QA and AI Quality Engineering roles.

The course costs $60 per week. Payment can be sent through Venmo, Zelle, or PayPal.

The course runs for 24 weeks and includes live instruction, practical discussion, exercises, quizzes, homework, materials, and interview-focused preparation.

You can register using the online registration form on this page. After submitting your name and email, the registration request is sent for processing.

You can also contact the course directly by email at register@alex.academy.