Unlock the power of LLM evaluation and build AI applications that are not only intelligent—but also reliable, efficient, and cost-effective. This comprehensive course teaches you how to evaluate large language model outputs across the entire development lifecycle—from prototype to production. Whether you’re an AI engineer, product manager, or ML ops specialist, this program gives you the tools to drive real impact with LLM-driven systems.

Modern LLM applications are powerful, but they’re also prone to hallucinations, inconsistencies, and unexpected behavior. That’s why evaluation is not a nice-to-have—it’s the backbone of any scalable AI product. In this hands-on course, you’ll learn how to design, implement, and operationalize robust evaluation frameworks for LLMs. We’ll walk you through common failure modes, annotation strategies, synthetic data generation, and how to create automated evaluation pipelines. You’ll also master error analysis, observability instrumentation, and cost optimization through smart routing and monitoring.

What sets this course apart is its focus on practical labs, real-world tools, and enterprise-ready templates. You won’t just learn the theory of evaluation—you’ll build test suites for RAG systems, multi-modal agents, and multi-step LLM pipelines. You’ll explore how to monitor models in production using CI/CD gates, A/B testing, and safety guardrails. You’ll also implement human-in-the-loop (HITL) evaluation and continuous feedback loops that keep your system learning and improving over time.

You’ll gain skills in annotation taxonomy, inter-annotator agreement, and how to build collaborative evaluation workflows across teams. We’ll even show you how to tie evaluation metrics back to business KPIs like CSAT, conversion rates, or time-to-resolution—so you can measure not just model performance, but actual ROI.

As AI becomes mission-critical in every industry, the ability to run scalable, automated, and cost-efficient LLM evaluations will be your edge. By the end of this course, you’ll be equipped to design high-quality evaluation workflows, troubleshoot LLM failures, and deploy production-grade monitoring systems that align with your company’s risk tolerance, quality thresholds, and cost constraints.

This course is perfect for:

• AI engineers building or maintaining LLM-based systems

• Product managers responsible for AI quality and safety

• MLOps and platform teams looking to scale evaluation processes

• Data scientists focused on AI reliability and error analysis

Join now and learn how to build trustable, measurable, and scalable LLM applications—from the inside out.

Für wen eignet sich dieser Kurs:

• AI/ML engineers building or fine-tuning LLM applications and workflows
• Product managers responsible for the performance, safety, and business impact of AI features
• MLOps and infrastructure teams looking to implement evaluation pipelines and monitoring systems
• Data scientists and analysts who need to conduct systematic error analysis or human-in-the-loop evaluation
• Technical founders, consultants, or AI leads managing LLM deployments across organizations
• Anyone curious about LLM performance evaluation, cost optimization, or risk mitigation in real-world AI systems