AI Evals Certification is a practical course on testing, evaluating the quality, and monitoring generative AI systems. This training helps build a systematic approach to AI Evals, identify weaknesses in LLM products, reduce risks before launch, and confidently improve AI systems after production release.
About the AI Evals Certification Course
The course focuses on evaluating and quality controlling modern GenAI products: chatbots, AI assistants, RAG systems, agent solutions, multimodal models, and other products based on large language models. Participants will learn how to design evals before release, select metrics, collect test datasets, and use evaluation results for making product and engineering decisions.
What You Will Learn
- Build the AI Evals process within a product, ML, or QA team.
- Determine quality criteria for LLM and GenAI systems considering business objectives.
- Choose between manual, semi-automated, and automated evaluation of AI products.
- Develop qualitative and quantitative metrics for evaluating model responses.
- Create test datasets and scenarios to check AI system behavior.
- Use the LLM-as-a-Judge approach and understand its limitations.
- Track quality degradation, errors, hallucinations, and other failures post-release.
- Apply online evals for monitoring AI systems in production.
- Link evaluation results with product analytics and business impact.
- Implement protective mechanisms, governance approaches, and risk controls.
Training Program
Basics of AI Evals and Quality of GenAI Products
The first part of the course covers principles of evaluating generative AI systems, typical errors of LLM products, and the role of evals in the development lifecycle. Participants will explore why standard QA is often insufficient for AI systems and how to design criteria for successful model behavior in advance.
Metrics, Criteria, and Test Datasets
This block is dedicated to developing metrics and evaluation sets. You will learn how to form datasets for testing, segment user scenarios, evaluate accuracy, completeness, relevance, safety, usefulness, and compliance of responses with expected requirements.
Manual and Automated Evaluation
The course helps understand when to use expert manual annotation, when automated checking is sufficient, and when a hybrid approach is needed. The advantages and limitations of automated evals are discussed separately, including evaluation using LLM-as-a-Judge.
Monitoring AI Systems in Production
Participants will learn how to monitor model behavior after launch: detect degradation, analyze user queries, control response quality, document incidents, and make decisions about rollback, refinement, or scaling of changes.
Evaluation-Driven Development
The practical part of the course introduces the Evaluation-Driven Development approach, where evaluation becomes part of AI product development. Testing results are used to prioritize improvements, verify hypotheses, control releases, and consistently enhance system quality.
AI Governance, Risks, and Security
A separate module is dedicated to risk management, requirements for safe AI implementation, control of undesirable model behavior, and creation of protective mechanisms. It also covers modern approaches to AI Governance and AI system quality at the organizational level.
Practical Part of the Course
During the training, participants complete a full practical project on AI Evals: from setting goals and choosing metrics to building the evaluation process and analyzing results. This format helps not only to study theory but also to apply it to real testing and AI product improvement tasks.
- Develop quality criteria for AI systems.
- Create test scenarios and an evaluation dataset.
- Set up manual or automated evaluation.
- Analyze results and identify problematic areas.
- Prepare recommendations for model or product improvement.
- Apply EDD for further development iterations.
Bonus Topics and Expert Analyses
In addition to the main program, the course includes additional materials and expert lectures on current directions in AI system evaluation. Participants will learn about modern tools and approaches used in teams creating GenAI products.
- LLM-as-a-Judge and features of using language models for response evaluation.
- Multimodal evals for systems working with text, images, and other data formats.
- Third-party vendor tools for monitoring, testing, and analyzing AI quality.
- AI Governance and organizational processes for quality control.
- Evaluation of business impact and association of AI Evals with product metrics.
Who the Course is Suitable For
- Product Leaders and Product Managers responsible for launching and developing GenAI products.
- Data Scientists and ML Engineers who need to systematically evaluate model and AI pipeline quality.
- QA and Test Engineers transitioning to testing LLM, AI assistants, and generative systems.
- AI Engineers and Developers who implement evaluation in the development process.
- Team Leaders who need to manage risks, quality, and reliability of AI solutions.
Results After Completing the Course
After training, you will be able to build a clear and reproducible process for evaluating AI systems: determine what needs to be measured, what data to use, how to interpret the results, and make decisions based on them.
- Understand how to test LLM and GenAI products before and after release.
- Design an eval system for specific business and user scenarios.
- Identify hallucinations, irrelevant responses, unsafe behavior, and quality degradation.
- Use metrics and analytics to improve AI products.
- Organize monitoring of AI systems in an industrial environment.
- Apply AI Evals as a constant quality improvement process, not a one-time check.
Why It's Important to Study AI Evals
Generative AI systems can behave unpredictably: provide inaccurate responses, change quality after model updates, perform poorly in certain query segments, or create business risks. Therefore, evaluating AI systems becomes a key stage in the development and operation of AI products.
AI Evals Certification helps acquire practical skills necessary for the safe launch, quality control, and continuous improvement of AI systems in real conditions.
