You've integrated a powerful Large Language Model (LLM) into your application. The initial results are impressive, and your team is excited. But then the hard questions start. Is the new prompt really better than the old one, or does it just "feel" better? How do you prove to stakeholders that switching from GPT-3.5 to GPT-4 is worth the extra cost? When you have two models that give slightly different answers, how do you decide which one is objectively superior?
Evaluate & Optimize LLM Performance
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Evaluate & Optimize LLM Performance
This course is part of LLM Optimization & Evaluation Specialization
Instructor: LearningMate
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Intermediate level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Evaluate LLMs using metrics like BLEU & ROUGE run A/B tests for statistical significance, and optimize model performance with data-driven strategies.
Skills you'll gain
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December 2025
Taught in English
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This course is part of the LLM Optimization & Evaluation Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
This introductory module lays the groundwork for quantitative Large Language Mode (LLM) evaluation. Learners will discover why relying on intuition to judge model performance is unsustainable and explore the foundational metrics used to create automated, objective evaluation systems. We will cover both lexical similarity metrics (like BLEU and ROUGE-L) that assess text structure and semantic metrics (like cosine similarity) that capture meaning. By the end of this module, learners will have the conceptual understanding and practical code to build their first automated evaluation script.
What's included
2 videos1 reading1 assignment1 ungraded lab
This module transitions from raw metrics to credible conclusions. Learners will discover why statistical rigor is non-negotiable when comparing LLM outputs. They will learn to formulate clear hypotheses, design and analyze A/B tests, and interpret results such as p-values and confidence intervals to distinguish true performance gains from random noise. By the end of this module, learners will be equipped to make data-driven decisions with confidence, ensuring that changes to prompts, models, or parameters lead to statistically significant improvements.
What's included
3 videos1 reading1 assignment1 ungraded lab
This module transitions from raw metrics to credible conclusions. Learners will discover why statistical rigor is non-negotiable when comparing LLM outputs. They will learn to formulate clear hypotheses, design and analyze A/B tests, and interpret results such as p-values and confidence intervals to distinguish true performance gains from random noise. By the end of this module, learners will be equipped to make data-driven decisions with confidence, ensuring that changes to prompts, models, or parameters lead to statistically significant improvements.
What's included
3 videos1 reading1 assignment1 ungraded lab
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