AI Engineering Fundamentals: Building Evaluation Harnesses

Evaluating AI models and LLM outputs requires systematic testing, but running evaluations manually is slow and inconsistent. To build reliable AI applications, you need an automated evaluation harness that can run multiple test cases and aggregate results. In this text-only course, you will learn how to design and build a programmatic evaluation harness from scratch. You will understand how to structure your test data, loop through test cases, aggregate evaluation metrics, and write those results to persistent storage for analysis. What you'll learn: • Understand the core concepts of AI evaluation and why programmatic harnesses are essential. • Design structured test cases using modern JSON schemas to ensure consistent inputs. • Build robust execution loops to run evaluations across multiple scenarios systematically. • Aggregate test results into structured arrays to calculate performance metrics. • Save evaluation outputs to JSON files for downstream analysis and version control. • Apply modern performance best practices to optimize test execution speed. You will start with foundational evaluation concepts before writing your first test loop. From there, you will progress to aggregating results, handling errors, and exporting structured files for performance analysis. This course is designed for beginner software engineers, aspiring AI developers, and data practitioners who want to systematically test their AI integrations, with no advanced machine learning background required. Start building your automated AI evaluation pipeline today.