[69% Off] 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.

Added on September 3, 2025 Development 3 min read

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 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: