Evaluating AI Agents: From Metrics to Real-World Impact

Name: Evaluating AI Agents: Metrics that Matter in Practice
Price: 36 USD

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.

2 hours of content 26 students

Start for Free

What you get:

2 hours of content
22 Interactive exercises
17 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Evaluating AI Agents: From Metrics to Real-World Impact

A course by Burcin Sarac

Start for Free

What you get:

2 hours of content
22 Interactive exercises
17 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Start for Free

What you get:

2 hours of content
22 Interactive exercises
17 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

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

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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.

Curriculum

1. Welcome & Foundations

3 Lessons 8 Min

Get oriented with the course vision, goals, and setup. Understand why evaluating AI agents matters and what you’ll achieve throughout the course.

What This Course Covers
3 min
Why Evaluating AI Agents is Critical & Our Course Roadmap
3 min
Learning Objectives and Setup Essentials Read now
2 min
2. Core Evaluation Principles

5 Lessons 17 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 Read now
4 min
The AI Evaluation Lifecycle: From Idea to Impact
3 min
Fundamental Metrics – Precision, Recall, F1-Score, and Accuracy Read now
6 min
Introduction to Qualitative Evaluation Concepts Read now
3 min
Downloads & Recap Read now
1 min
3. Quantitative Metrics & Benchmarking

4 Lessons 13 Min

Dive into key metrics for generative and classification-based agents. Understand industry benchmarks and practice calculating metrics in Python.

Deep Dive: Metrics for Generative AI Read now
4 min
Metrics for Classification/Understanding in Agents & Intro to Industry Benchmarks Read now
4 min
Coding Exercise: Calculating Text Similarity & Generation Metrics Read now
4 min
Downloads & Recap Read now
1 min
4. Evaluating LLM-Powered Agents

3 Lessons 14 Min

Uncover the unique challenges of evaluating large language models (LLMs) and how to assess chatbot effectiveness and task performance.

Unique Challenges in Evaluating LLMs Read now
4 min
Evaluating Chatbot & Q&A Effectiveness Read now
9 min
Downloads & Recap Read now
1 min
5. Mastering RAG System Evaluation

6 Lessons 19 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
4 min
Evaluating the Retriever – Are We Finding the Right Stuff? Read now
6 min
Evaluating the Generator: Is the Answer Good and Faithful? Read now
4 min
End-to-End RAG Evaluation Strategies & Human-in-the-Loop Read now
3 min
Coding Exercise: Retriever Evaluation in Python Read now
1 min
Downloads & Recap Read now
1 min
6. Human-Centric Evaluation Approaches

4 Lessons 12 Min

Focus on gathering and designing human feedback loops. Learn to build simple mechanisms and extract insights from qualitative data.

The Importance of Human Feedback & Overview of Key Methods
3 min
Principles for Designing Simple User Feedback Mechanisms Read now
4 min
Brief on Analyzing Qualitative Data: Identifying Themes Read now
4 min
Downloads & Recap Read now
1 min
7. Ethical Considerations in AI Evaluation

4 Lessons 12 Min

Evaluate AI systems responsibly. Learn how to identify bias, assess safety, and incorporate fairness using modern ethical frameworks and red teaming.

Introduction to AI Ethics in Evaluation
3 min
Identifying Bias and Ensuring Safety in AI Agents Read now
5 min
Overview of Responsible AI Frameworks & Red Teaming Concepts Read now
3 min
Downloads & Recap Read now
1 min
8. Practical Evaluation Workflows & Future Outlook

4 Lessons 10 Min

Build evaluation pipelines and explore how to improve systems post-launch. Look ahead at emerging methods for multi-agent systems and continuous evaluation.

Building Basic Evaluation Pipelines & Leveraging Libraries Read now
3 min
Connecting Evaluation to Improvement Read now
3 min
Future Outlook: Evaluating Multi-Agent Systems & Lifelong Learning Read now
3 min
Downloads & Recap Read now
1 min
9. Capstone Project & Course Conclusion

3 Lessons 4 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.

Capstone Project Overview: Bringing It All Together Read now
2 min
Presenting Evaluation Findings & Course Recap Read now
1 min
Final Encouragement & Next Steps in Your Learning Journey Read now
1 min

Topics

Machine LearningDeep LearningData ScienceCloud ComputingNatural Language ProcessingAILangchainHuggingface

Tools & Technologies

Course Requirements

Working knowledge of Python (functions, dictionaries, basic libraries like pandas)
Basic understanding of machine learning workflows
No prior experience with AI evaluation frameworks needed

Who Should Take This Course?

Level of difficulty: Intermediate

AI developers and ML engineers seeking to improve model assessment
Data scientists working with NLP, retrieval, or production models
Product managers aiming to align AI performance with user experience
Researchers and evaluators focused on fairness, bias, and real-world impact

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Burcin Sarac

Toptal

1 Courses

0 Reviews

26 Students

I’m an AI Consultant with hands-on experience delivering production-grade AI solutions through Toptal, where I’ve contributed to projects spanning construction, real estate, entertainment, and SaaS. My expertise includes LLM pipelines, RAG architectures, and agentic AI using tools like LangGraph, LlamaIndex, and GCP services. In addition to consulting, I lead and founded Custom Craft Bot (CCB) an AI consultancy and SaaS venture. CCB offers both custom AI development and a social media automation platform powered by LLMs, enabling content generation, engagement, and trend-aware interaction. This dual focus allows me to support businesses with both tailored AI systems and ready-to-use intelligent tools.

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