Kiram A.
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Online Course
free
Evaluating AI Agents: From Metrics to Real-World Impact
with
Burcin Sarac
Learn to evaluate AI systems beyond accuracy. This hands-on course covers practical metrics, real-world case studies, and responsible evaluation strategies for chatbots, RAG models, and beyond.
192 students already have enrolled
Skill level:
Intermediate
Duration:
2 hours
-
Lessons (2 hours)
- Lessons (2 hours)
CPE credits:
3
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Measure AI performance using both quantitative and qualitative metrics
- Evaluate chatbots, classifiers, RAG systems, and lifelong learning agents
- Apply real-world metrics like Goal Success Rate, Context Recall, and F1
- Identify and mitigate issues like hallucination, bias, and evaluation drift
- Design human-in-the-loop and task-based evaluation workflows
- Connect model evaluation with continuous improvement strategies
- Navigate responsible AI principles including fairness and explainability
Topics & tools
machine learningdeep learningdata sciencecloud computingnatural language processingaiLangChainhuggingfacepython
Your instructor
Track record
Bringing real-world expertise from leading global companies
Academic background
Athens University of Economics and Business
Course OVERVIEW
Description
CPE Credits: 3
Field of Study: Specialized Knowledge
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
Welcome to this practical, insight-driven course on evaluating AI agents, where metrics meet real-world impact.
You’ll explore what it really means to measure AI performance from basic accuracy and precision to advanced concepts like Goal Success Rate, Context Recall, and Human-in-the-Loop evaluation. We’ll break down both quantitative and qualitative approaches to assess models in natural language processing, classification, retrieval-augmented generation (RAG), and more.
Through hands-on examples, industry-informed cases, and real-world failures, you’ll learn how to evaluate chatbots, recommendation systems, face detection tools, and lifelong learning agents. You'll also uncover how fairness, explainability, and user feedback shape truly responsible AI.
By the end, you'll have the tools and mindset to go beyond the leaderboard and design evaluations that actually matter in production. Whether you're an AI developer, product manager, or researcher, this course helps you confidently bridge metrics with meaning.
Let’s get started and redefine how we evaluate AI, one agent at a time.
Prerequisites
- Working knowledge of Python (functions, dictionaries, basic libraries like pandas)
- Basic understanding of machine learning workflows
- No prior experience with AI evaluation frameworks needed
Advanced preparation
- None
Curriculum
36 lessons
22 exercises
1 exam
- 2. Core Evaluation Principles17 Min
Explore what AI evaluation really means. Learn core metrics like precision, recall, and F1, and why qualitative evaluation is just as critical.
Defining AI Agents & The Nuance of 'Good' Evaluation FreeExercise FreeThe AI Evaluation Lifecycle: From Idea to Impact FreeExercise FreeFundamental Metrics – Precision, Recall, F1-Score, and Accuracy FreeExercise FreeIntroduction to Qualitative Evaluation Concepts FreeExercise FreeDownloads & Recap Free - 3. Quantitative Metrics & Benchmarking13 Min
Dive into key metrics for generative and classification-based agents. Understand industry benchmarks and practice calculating metrics in Python.
- 4. Evaluating LLM-Powered Agents14 Min
Uncover the unique challenges of evaluating large language models (LLMs) and how to assess chatbot effectiveness and task performance.
- 5. Mastering RAG System Evaluation19 Min
Learn how to evaluate retrieval-augmented generation (RAG) systems from both retriever and generator perspectives, including coding exercises and human-in-the-loop strategies.
The RAG Pipeline & Key Evaluation Points FreeEvaluating the Retriever – Are We Finding the Right Stuff? FreeExercise FreeEvaluating the Generator: Is the Answer Good and Faithful? FreeExercise FreeEnd-to-End RAG Evaluation Strategies & Human-in-the-Loop FreeExercise FreeCoding Exercise: Retriever Evaluation in Python FreeDownloads & Recap Free - 6. Human-Centric Evaluation Approaches12 Min
Focus on gathering and designing human feedback loops. Learn to build simple mechanisms and extract insights from qualitative data.
- 7. Ethical Considerations in AI Evaluation12 Min
Evaluate AI systems responsibly. Learn how to identify bias, assess safety, and incorporate fairness using modern ethical frameworks and red teaming.
- 8. Practical Evaluation Workflows & Future Outlook10 Min
Build evaluation pipelines and explore how to improve systems post-launch. Look ahead at emerging methods for multi-agent systems and continuous evaluation.
- 9. Capstone Project & Course Conclusion4 Min
Apply everything you’ve learned in a final project. Present your evaluation strategy, reflect on your journey, and get inspired for next steps in AI development.
- 10. Course exam40 Min
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