AI Red Teaming Test, Evaluate & Improve AI Systems

Learn AI red teaming, identify AI failures, test model behavior, assess risks, and report findings professionally.
What you'll learn

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Identify and analyze common AI failure modes, including hallucinations, reasoning errors, and instruction-following failures.
Design practical red-team tests and challenging prompts to evaluate AI system behavior and reliability.
Evaluate AI responses using structured rubrics, evidence, severity levels, and risk-based scoring. Поле 4
Document AI failures and create clear, professional red-team findings with actionable recommendations.
Requirements

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No programming or advanced technical knowledge is required. Basic computer skills and an interest in AI are sufficient.
Description

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This course contains the use of artificial intelligence.
Course Description

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This course provides a practical introduction toAI Red Teaming - the systematic process of testing AI systems, identifying weaknesses, evaluating failures, and documenting findings.
You will learn how to examine AI-generated responses beyond surface-level quality and identify problems that may remain hidden behind confident or polished language.
Throughout the course, you will explore important AI failure modes includinghallucinations, factual errors, instruction-following failures, reasoning problems, bias, unsafe responses, and hidden omissions.
You will also learn how professional red teamers design effective test prompts, use adversarial testing techniques, compare AI responses, assess the severity and potential impact of failures, and document findings in a clear and evidence-based red team report.
The course includes practical examples and structured exercises designed to help you develop a systematic approach to AI evaluation and red teaming.
By the end of the course, you will understand how to
Design purposeful red team tests rather than rely on random prompting
Identify obvious and hidden failures in AI-generated responses
Test instruction following, reasoning, factuality, bias, and safety
Compare multiple AI responses objectively
Assess failure severity and potential impact
Document evidence and create professional red team reports
Apply a structured red teaming workflow to realistic AI scenarios
No previous AI red teaming experience is required. The course is designed to be accessible to beginners while introducing methods that reflect real AI evaluation and testing workflows.
AI Transparency Notice
This course was developed and directed byMarina Langer with the assistance of generative AI tools. AI technology was used to support the creation of selected educational materials, visuals, narration, and video content. The course structure, learning objectives, topic selection, instructional direction, review, and final editorial decisions were overseen by the instructor.
The goal is not simply to find mistakes in AI systems, but to understand their behavior throughsystematic, objective, and evidence-based testing.
Test systematically. Judge objectively. Document with evidence.
Who this course is for

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Beginners who want to develop practical skills in AI evaluation, testing, and red teaming.
AI Evaluators, data annotators, QA professionals, and aspiring AI safety specialists who want to expand their skills.
Professionals and career changers interested in testing AI systems and building practical AI evaluation portfolio projects.
Homepage

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