# AI Evaluation Engineering: LLM Testing, Benchmarking, and Regression Training > Source: https://sukruyusufkaya.com/en/training/ai-evaluation-engineering-llm-test-benchmark-ve-regression-egitimi > Updated: 2026-06-13T02:30:14.594Z > Level: advanced > Topics: AI Evaluation Engineering, LLM Testing, Benchmark Design, Golden Set, Rubric-Based Evaluation, Judge-Based Evaluation, Pairwise Comparison, Regression Testing, Release Gates, Quality Assurance, RAG Evaluation, Agent Evaluation, Tool Selection Accuracy, Groundedness, Citation Quality, Observability, Runtime Quality Signals, Failure Analysis, AI Governance, Production Quality **TLDR:** An advanced AI evaluation engineering training for enterprises covering benchmark design, golden-set creation, rubric-based evaluation, regression testing, release gates, RAG-agent evaluation, and runtime quality signals together. ## Açıklama AI Evaluation Engineering: LLM Testing, Benchmarking, and Regression Training is an advanced and intensive program designed to help companies evaluate generative AI systems not through impressive demo outputs alone, but through measurable quality, systematic benchmarking discipline, pre-release quality gates, regression control, security, and production behavior. The training treats evaluation not as an extension of classical software testing, but as a new quality-engineering discipline that jointly manages prompts, models, retrieval, agent behavior, tool selection, groundedness, task success, style compliance, policy compliance, failure-mode analysis, and production telemetry. Throughout the program, participants systematically learn why an LLM system cannot be considered successful merely because it “appears to answer correctly,” which quality metrics are meaningful for which use cases, the difference between offline evaluation and user behavior observed online, how benchmark datasets should be prepared, how golden sets and rubrics should be designed, when judge-based evaluation is appropriate, how pairwise comparison and rubric-based evaluation patterns work, how regression suites should be built, how to measure the quality impact of prompt or model changes, how release-gate approaches should be established, which additional evaluation layers are required for RAG and agent systems, how safety and compliance risks should be included in evaluation frameworks, and how observability and runtime-quality signals should be interpreted together. This training addresses several critical needs: companies cannot safely release prompt changes or model updates in GenAI projects; quality is judged through only a few sample outputs; benchmark sets are weak, unbalanced, or disconnected from real use cases; product, data, and engineering teams define quality in different languages; regression risks are detected too late; retrieval and generation failures are conflated in RAG systems; task success and tool-selection failures cannot be separated in agent systems; security and policy violations cannot be measured systematically; and production quality degradation cannot be managed without observability. The program focuses exactly on these bottlenecks and teaches an evaluation-engineering approach that makes enterprise AI quality measurable, observable, and governable. A major differentiator of the program is that it does not view evaluation as simply running test data. Participants see that a strong evaluation-engineering approach must jointly address success-criteria design, dataset quality, rubric clarity, metric selection, regression logic, offline-online signal relationships, release governance, observability, and continuous-improvement loops. For that reason, the training is built not around “running evaluations,” but around building an engineering discipline that manages product quality by measuring the right thing, in the right way, at the right time. By the end of the training, participants gain an evaluation-engineering perspective that enables them to build meaningful quality frameworks for different GenAI products, prepare evaluation datasets and benchmark scenarios systematically, manage regression risks before and after release, separate quality dimensions more accurately for RAG and agent systems, combine observability and runtime-quality signals with evaluation logic, and develop enterprise AI products in a safer, more measurable, and more sustainable way. ## Kazanımlar - Build meaningful quality frameworks for different GenAI products. - Prepare benchmark datasets and golden-set structures systematically. - Manage regression risks across prompt, model, retrieval, and agent changes. - Make more controlled deployment decisions through release gates and quality thresholds. - Separate quality problems more accurately in RAG and agent systems. - Develop a more mature evaluation-engineering approach that interprets offline evaluation together with runtime quality signals.

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.