AI Evaluation Engineering: LLM Testing, Benchmarking, and Regression Training

Detailed Content (EN)

This training is designed for organizations that want to evaluate generative AI systems not through a few successful sample outputs, but through a systematic and defensible engineering discipline. At the center of the program is one core idea: an LLM or GenAI system cannot be considered production-ready merely because it works technically. Real quality is determined by what is measured, how it is measured, with which data it is measured, how the results are interpreted against thresholds, how changes affect quality, and how these measurements influence release decisions. For that reason, the training addresses benchmark design, evaluation datasets, rubrics, metrics, regression, release gates, observability, and runtime quality signals together.

Throughout the training, participants see why evaluation engineering differs fundamentally from classical software testing. In LLM-based systems, correctness is not always binary; the same output may be considered successful or unsuccessful depending on the use case. In one application, task completion may be the most critical metric; in another, groundedness, citation correctness, style compliance, or policy compliance may matter more. For that reason, the program moves beyond a “single-metric quality” mindset and teaches multi-layered quality design. This enables teams to define meaningful quality frameworks for their own products.

One of the strongest aspects of the program is its emphasis on benchmark and dataset engineering. Participants systematically learn topics such as golden-set construction, data sampling, edge-case collection, failure-bucket design, risks of imbalanced samples, benchmark stratification, and use-case-specific test coverage design. In this way, evaluation is treated not simply as running tests, but as building the right evaluation universe. In addition, rubric design, judge-based evaluation, pairwise comparison, and structured scoring make it possible to build more consistent and explainable evaluation frameworks.

The second major pillar of the program is regression and release governance. Participants learn how to re-evaluate quality after prompt changes, system-instruction updates, model transitions, retrieval adjustments, tool-behavior changes, or guardrail modifications. Regression-suite logic, release-gate thresholds, deployment-blocking criteria, rollback triggers, and post-release monitoring signals are covered in depth. In this way, quality becomes not merely a retrospective metric, but an active engineering mechanism that drives release decisions.

The program also covers evaluation layers specific to RAG and agent systems. Participants learn how to separate retrieval success from generation quality, how to measure citation correctness and source-usage quality, how to assess tool-selection accuracy, how to distinguish step success from task success, how to evaluate planning reliability, and how to analyze memory-related failure patterns. As a result, the training covers not only core LLM answer quality, but also the multi-layered evaluation needs of modern enterprise GenAI systems.

Finally, the program connects observability and runtime quality signals to evaluation engineering. It addresses in detail how to read user feedback, production logs, degradation patterns, guardrail hit rates, fallback frequency, latency degradations, and other operational signals linked to quality. In this way, evaluation becomes not merely an offline lab activity, but a living quality system that informs production decisions.