Mastering LLM Evaluation: Build Reliable Scalable AI Systems

Master the art and science of LLM evaluation with hands-on labs, error analysis, and cost-optimized strategies.

What you'll learn
- Understand the full lifecycle of LLM evaluation—from prototyping to production monitoring
- Identify and categorize common failure modes in large language model outputs
- Design and implement structured error analysis and annotation workflows
- Build automated evaluation pipelines using code-based and LLM-judge metrics
- Evaluate architecture-specific systems like RAG, multi-turn agents, and multi-modal models
- Set up continuous monitoring dashboards with trace data, alerts, and CI/CD gates
- Optimize model usage and cost with intelligent routing, fallback logic, and caching
- Deploy human-in-the-loop review systems for ongoing feedback and quality control

Requirements
- No prior experience in evaluation required—this course starts with the fundamentals
- Basic understanding of how large language models (LLMs) like GPT-4 or Claude work
- Familiarity with prompt engineering or using AI APIs is helpful, but not required
- Comfort reading JSON or working with simple scripts (Python or notebooks) is a plus
- Access to a computer with internet connection (for labs and dashboards)
- Curiosity about building safe, measurable, and cost-effective AI systems!

Description
Unlock the power ofLLM evaluationand build AI applications that are not only intelligent—but also reliable, efficient, and cost-effective. This comprehensive course teaches you how toevaluate large language model outputsacross 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 withLLM-driven systems.

ModernLLM applicationsare powerful, but they're also prone tohallucinations,inconsistencies, andunexpected 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 robustevaluation frameworks for LLMs. We’ll walk you through common failure modes, annotation strategies, synthetic data generation, and how to create automatedevaluation pipelines. You’ll also mastererror analysis, observability instrumentation, andcost optimizationthrough smart routing and monitoring.

What sets this course apart is its focus onpractical labs, real-world tools, andenterprise-ready templates. You won’t just learn the theory of evaluation—you’ll build test suites forRAG systems, multi-modal agents, andmulti-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 implementhuman-in-the-loop (HITL) evaluationand continuous feedback loops that keep your system learning and improving over time.

You’ll gain skills inannotation taxonomy,inter-annotator agreement, and how to build collaborative evaluation workflows across teams. We’ll even show you how to tie evaluation metrics back tobusiness KPIslike 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 runscalable, automated, and cost-efficient LLM evaluationswill 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 maintainingLLM-based systems

Product managers responsible for AI quality and safety

MLOps and platform teams looking to scale evaluation processes

Data scientists focused onAI reliability and error analysis

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

Who this course is for:
- 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
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