Learn proven methods for quickly improving AI applications. Build AI systems that outperform competitors, regardless of the specific use case.
If you encounter questions like these while working with AI:
How to test applications where results are probabilistic and require subjective evaluation?
If I change a prompt, how can I ensure nothing else breaks?
Where should engineering efforts be directed? Is it necessary to test everything?
What to do if there is no data or users - where to start?
Which metrics should be tracked? What tools should be used? Which models should be selected?
Is it possible to automate testing and evaluation? And if yes, how can you trust it?
- then this course is for you.
This is a practical course for engineers and technical product managers. Ideal for those who know how to program or "enjoy coding by intuition."
What to Expect
You will experience intensive practice: exercises, working with code and data. We meet twice a week for four weeks + we offer generous office hours. All sessions are recorded and will be available in an asynchronous format.
Course Content
Basics and lifecycle of LLM application evaluation
Systematic error analysis
Building effective metrics and automated evaluation pipelines
Collaborative practices and alignment of evaluation criteria
Testing strategies for different architectures (RAG, pipelines, multimodal systems, etc.)
Monitoring in production and continuous quality evaluation
Organizing an effective human-in-the-loop review process
Cost optimization and query routing
Learning Outcomes
Master the best tools for finding, diagnosing, and prioritizing errors in AI.
Learn how to use synthetic data before user engagement and how to use real data as effectively as possible.
Build a "data flywheel" that ensures your AI improves over time.
Learn to automate parts of the evaluation processes and trust them.
Be able to customize AI to your preferences and requirements.
Avoid common mistakes accumulated from the experience of more than 35 AI projects.
Gain practical experience through end-to-end exercises, code, and analysis of real cases.
Hamel Husain is a US ML engineer (formerly at Airbnb and GitHub, now at Parlance Labs), a fast.ai contributor, and one of the most visible independent voices on the production-engineering side of LLM systems — particularly around evals, fine-tuning, and the workflow that connects model training to deployed product features.
His CourseFlix listing carries AI Evals For Engineers & PMs. Material is paid and aimed at engineers and product managers shipping LLM-powered features who need to evaluate model output systematically rather than by gut.
Shreya Shankar is a US ML engineer and PhD candidate (UC Berkeley, formerly Google Brain and Viaduct) focused on the production-engineering side of ML systems and LLM evals. She is one of the more cited independent voices on the eval discipline for AI applications.
Her CourseFlix listing carries AI Evals For Engineers & PMs — a structured treatment of the eval discipline applied to LLM applications: how to design eval datasets, choose appropriate metrics, run systematic comparisons, and use evals as a continuous-feedback tool rather than a one-off launch gate.
Material is paid and aimed at engineers and product managers shipping LLM-powered features. For broader content, see CourseFlix's AI for Business & Product category page.
Watch Online 41 lessons
This is a demo lesson (10:00 remaining)
You can watch up to 10 minutes for free. Subscribe to unlock all 41 lessons in this course and access 10,000+ hours of premium content across all courses.
Learn to leverage AI to optimize your freelancing profile for visibility, craft personalized and persuasive proposals, and manage projects more efficiently.
An advanced course on the interaction of AI and design systems. Learn methods for implementing modern technologies to improve the quality of digital products.
8h 15m5/5
Frequently asked questions
What are the prerequisites for this course?
The course is designed for engineers and technical product managers who have a foundational understanding of programming. It is ideal for individuals who enjoy coding by intuition and are looking to improve their skills in evaluating AI applications. No advanced AI knowledge is required, but familiarity with basic programming concepts will be beneficial.
What types of projects will I work on during the course?
Throughout the course, you'll engage in practical exercises such as building custom evaluation tools with coding agents and working on case studies like 'From Vibe Checks to Evals to Feedback Loops'. These projects are designed to enhance your understanding of AI application evaluation and help you develop systems that outperform competitors.
Who is the target audience for this course?
This course is targeted at engineers and technical product managers who are involved in the development and improvement of AI applications. It is particularly beneficial for those who face challenges in evaluating AI systems and are looking for methods to improve AI application performance and reliability.
How does this course compare in depth and scope to similar courses?
The course offers a practical approach to AI evaluation, focusing on real-world applications and systematic error analysis. Unlike other courses that may provide a broad overview of AI, this course delves into specific tools and techniques such as automated evaluators and continuous human review systems, providing a deep dive into the evaluation lifecycle.
What specific tools or platforms are covered in the course?
The course covers a variety of tools and platforms essential for AI evaluation, including Braintrust, Arize Phoenix, and LangSmith. Tutorials and walkthroughs are provided for each, ensuring you gain hands-on experience in using these tools to streamline and automate the evaluation process.
What is not covered in the course?
The course does not cover foundational AI model building or basic programming instruction. It assumes a certain level of programming knowledge and focuses specifically on the evaluation and improvement of AI applications, rather than the initial development of AI models.
How much time should I expect to commit to this course?
While the total runtime of the course is not specified, it includes 41 lessons and several optional office hours. Given the practical nature of the course, students should anticipate dedicating additional time outside of the core lessons to engage with exercises and apply the techniques learned.
