Contact Us

Intermediate course

Evaluation for LLM Applications

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

Rating: 4.4442 ratings1,994 students1 total hour24 lectures
Open course in DoJo Back to courses
Evaluation for LLM Applications Intermediate

Course facts

  • Last updated 09/2025
  • English English [Auto], Korean [Auto] , 1 more
  • Instructor: Digital Innovation | Les Experts
  • technical implementation with AI models and applications

What you'll learn

Practical outcomes

  • 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.

Curriculum

9 sections • 24 lectures • 59m total length

Introduction2 lectures • 4min
  1. Introduction03:57
  2. Download Course Materials00:00
Section 1: Foundations of LLM Evaluation3 lectures • 7min
  1. Types of evaluations – intrinsic vs extrinsic02:23
  2. What makes an LLM "good"? (accuracy, helpfulness, safety, latency)02:30
  3. Challenges in evaluating generative outputs02:34
  4. Quiz – Section 1: Foundations of LLM Evaluation 5 questions
Section 2: Instrumentation & Observability3 lectures • 8min
  1. Logging LLM inputs, outputs, and metadata02:36
  2. Setting up observability pipelines (OpenTelemetry, Prometheus, etc.)02:23
  3. Metrics to track (latency, token usage, user satisfaction)02:33
  4. Quiz – Section 2: Instrumentation & Observability 5 questions
Section 3: Systematic Error Analysis3 lectures • 7min
  1. Categorizing LLM failures (hallucinations, bias, toxicity)02:23
  2. Root cause analysis frameworks02:28
  3. Feedback loops and error logging strategies02:28
  4. Quiz – Section 3: Systematic Error Analysis 5 questions
Section 4: Evaluation Techniques & LLM-Judge Approaches3 lectures • 7min
  1. Human evaluation vs automatic evaluation02:17
  2. Using LLMs to grade other LLMs (LLM-as-a-judge techniques)02:35
  3. Pairwise comparison and scoring methods02:21
  4. Quiz – Section 4: Evaluation Techniques & LLM-Judge Approaches 5 questions
Section 5: Evaluating RAG Systems3 lectures • 7min
  1. What makes Retrieval-Augmented Generation different?02:16
  2. Evaluating retrieval quality (recall, precision, relevance)02:22
  3. Combined evaluation of retrieval + generation02:21
  4. Quiz – Section 5: Evaluating RAG Systems 5 questions
Section 6: Production Monitoring & Continuous Evaluation3 lectures • 7min
  1. Designing evaluation in production environments02:05
  2. Integrating eval into CI/CD or workflow pipelines02:21
  3. Alerting, thresholds, and incident response02:27
  4. Quiz – Section 6: Production Monitoring & Continuous Evaluation 5 questions
Section 7: Human Review & Cost Optimization3 lectures • 7min
  1. Creating scalable human-in-the-loop review systems02:14
  2. Balancing eval quality vs budget constraints02:07
  3. Token and model selection strategies to reduce costs02:17
  4. Quiz – Section 7: Human Review & Cost Optimization 5 questions
Course Conclusion – Key Takeaways1 lecture • 6min
  1. Course Conclusion – Key Takeaways05:41

Who it is for

  • 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.

Course description

Overview

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.

Instructor

Digital Innovation | Les Experts

Digital Innovation | Les Experts By Dr. Firas | SMART E-LEARNING LLC Digital Innovation est un collectif d'experts freelance qualifiés dans le domaine des technologies web et mobile (architecture, intelligence artificielle et cloud). Nous vous proposons des formations diverses et variées faites, généralement technique, qui servent de référence pour les métiers du web. Pour ma part! Je suis docteur en informatique et intelligence artificielle. Je suis également professeur universitaire et chercheur permanent. En dehors de mes activités académiques, je suis également créateur de contenu numérique sur mesure. Avec une solide formation en informatique et en intelligence artificielle, je suis passionné par l'applicabilité de ces technologies à de nombreux domaines et j'aime partager mes connaissances et mon expertise avec les étudiants et les professionnels de tous horizons. J'espère pouvoir continuer à contribuer à l'avancement de ces domaines passionnants et à aider les gens à développer leurs compétences pour réussir dans leur carrière. Cette passion m’a permis de créer un portefeuille de plus de 200 formations dont 30 qui sont considérées Best-Seller sur la plateforme Udemy où je suis classé Meilleur Formateur.