AI Evals For Engineers, PMs

AI Evals For Engineers, PMs provides a comprehensive foundation in evaluating artificial intelligence systems, with particular emphasis on large language models and generative AI applications. This course addresses one of the most critical challenges facing AI practitioners today: how to systematically assess whether AI systems are performing as intended, meeting quality standards, and delivering value in production environments.

The learning journey begins with foundational concepts that establish why evaluation is essential in the AI development lifecycle. Students gain understanding of the unique challenges posed by non-deterministic AI systems, where traditional software testing approaches fall short. The course explores how evaluation frameworks serve as the backbone for responsible AI deployment, enabling teams to build confidence in their systems before releasing them to users.

As students progress, they learn to design evaluation frameworks from the ground up. This involves understanding different types of evaluations, from unit-level tests that assess specific model behaviors to system-level evaluations that examine end-to-end performance. The course teaches how to identify what matters most for a given application, translating business requirements and user needs into measurable evaluation criteria. Students work through the process of defining success metrics that align with product goals, whether those involve accuracy, relevance, safety, consistency, or other dimensions of performance.

The curriculum then moves into practical implementation of evaluation systems. Students learn to construct datasets specifically designed for testing AI behavior, including strategies for sampling representative examples, creating edge case collections, and building adversarial test sets that probe model weaknesses. The course covers both automated evaluation techniques, such as model-based scoring and rule-based checks, and approaches that incorporate human judgment for subjective quality dimensions.

A significant portion of the course focuses on interpreting evaluation results and turning insights into action. Students develop skills in analyzing patterns across evaluation metrics, identifying systematic failures, and diagnosing root causes of poor performance. The course teaches how to establish baselines, track improvements over time, and make informed decisions about when a model is ready for production or requires further development.

The course explores industry-standard benchmarking practices, examining how public benchmarks can inform development while understanding their limitations. Students learn to adapt existing benchmarks to their specific contexts and create custom benchmarks that reflect real-world use cases. This includes understanding the relationship between benchmark performance and actual user experience.

Integration of evaluation into development workflows receives dedicated attention. The course covers how to build continuous evaluation pipelines that run automatically as models and systems evolve, enabling rapid iteration while maintaining quality gates. Students learn to set up monitoring systems that track evaluation metrics in production, providing early warning signals when model performance degrades.

Cross-functional collaboration forms another key component of the curriculum. The course addresses how engineers and product managers can work together effectively using evaluation frameworks as a common language. Students learn to communicate evaluation results to diverse stakeholders, from technical teams who need diagnostic details to business leaders who care about impact metrics.

Advanced topics include evaluating for safety and alignment, assessing robustness across different user populations and use cases, and handling the challenges of evaluating creative or open-ended AI outputs where correct answers are not clearly defined. The course also covers cost-benefit considerations in evaluation design, helping students balance thoroughness with resource constraints.

Throughout the course, students engage with real-world scenarios that mirror the challenges faced by AI teams in production environments. The curriculum emphasizes practical decision-making, equipping students with frameworks they can immediately apply to their own projects. By the end of the course, students possess a systematic approach to AI evaluation that enables them to build more reliable, trustworthy, and effective AI systems.

Who this course is for: