Evaluation For Llm Applications

Learn practical LLM evaluation with error analysis, RAG systems, monitoring, and cost optimization.

What you'll learn

Understand core evaluation methods for Large Language Models, including human, automated, and hybrid approaches.

Apply systematic error analysis frameworks to identify, categorize, and resolve model failures.

Design and monitor Retrieval-Augmented Generation (RAG) systems with reliable evaluation metrics.

Implement production-ready evaluation pipelines with continuous monitoring, feedback loops, and cost optimization strategies.

Requirements

No strict prerequisites — basic knowledge of AI or software development is helpful but not required.

Description

Large Language Models (LLMs) are transforming the way we build applications — from chatbots and customer support tools to advanced knowledge assistants. But deploying these systems in the real world comes with a critical challenge: how do we evaluate them effectively?This course, Evaluation for LLM Applications, gives you a complete framework to design, monitor, and improve LLM-based systems with confidence. You will learn both the theoretical foundations and the practical techniques needed to ensure your models are accurate, safe, efficient, and cost-effective.We start with the fundamentals of LLM evaluation, exploring intrinsic vs extrinsic methods and what makes a model “good.” Then, you’ll dive into systematic error analysis, learning how to log inputs, outputs, and metadata, and apply observability pipelines. From there, we move into evaluation techniques, including human review, automatic metrics, LLM-as-a-judge approaches, and pairwise scoring.Special focus is given to Retrieval-Augmented Generation (RAG) systems, where you’ll discover how to measure retrieval quality, faithfulness, and end-to-end performance. Finally, you’ll learn how to design production-ready monitoring, build feedback loops, and optimize costs through smart token and model strategies.Whether you are a DevOps Engineer, Software Developer, Data Scientist, or Data Analyst, this course equips you with actionable knowledge to evaluate LLM applications in real-world environments. By the end, you’ll be ready to design evaluation pipelines that improve quality, reduce risks, and maximize value.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Download Course Materials

Section 2: Section 1: Foundations of LLM Evaluation

Lecture 3 Types of evaluations – intrinsic vs extrinsic

Lecture 4 What makes an LLM "good"? (accuracy, helpfulness, safety, latency)

Lecture 5 Challenges in evaluating generative outputs

Section 3: Section 2: Instrumentation & Observability

Lecture 6 Logging LLM inputs, outputs, and metadata

Lecture 7 Setting up observability pipelines (OpenTelemetry, Prometheus, etc.)

Lecture 8 Metrics to track (latency, token usage, user satisfaction)

Section 4: Section 3: Systematic Error Analysis

Lecture 9 Categorizing LLM failures (hallucinations, bias, toxicity)

Lecture 10 Root cause analysis frameworks

Lecture 11 Feedback loops and error logging strategies

Section 5: Section 4: Evaluation Techniques & LLM-Judge Approaches

Lecture 12 Human evaluation vs automatic evaluation

Lecture 13 Using LLMs to grade other LLMs (LLM-as-a-judge techniques)

Lecture 14 Pairwise comparison and scoring methods

Section 6: Section 5: Evaluating RAG Systems

Lecture 15 What makes Retrieval-Augmented Generation different?

Lecture 16 Evaluating retrieval quality (recall, precision, relevance)

Lecture 17 Combined evaluation of retrieval + generation

Section 7: Section 6: Production Monitoring & Continuous Evaluation

Lecture 18 Designing evaluation in production environments

Lecture 19 Integrating eval into CI/CD or workflow pipelines

Lecture 20 Alerting, thresholds, and incident response

Section 8: Section 7: Human Review & Cost Optimization

Lecture 21 Creating scalable human-in-the-loop review systems

Lecture 22 Balancing eval quality vs budget constraints

Lecture 23 Token and model selection strategies to reduce costs

Section 9: Course Conclusion – Key Takeaways

Lecture 24 Course Conclusion – Key Takeaways

DevOps Engineers who want to integrate LLM evaluation into production pipelines.,Software Developers interested in building reliable AI-powered applications.,Data Scientists looking to analyze and monitor model performance.,Data Analysts aiming to understand evaluation metrics and error patterns.,AI Practitioners seeking practical frameworks for testing and improving LLMs.,Tech Professionals who want to balance model quality, safety, and cost in real-world systems.