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Evaluating Large Language Model Outputs: A Practical Guide

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Evaluating Large Language Model Outputs: A Practical Guide

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Beginner level

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2 hours to complete
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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Identify the fundamentals of Large Language Models, including current evaluation methods and access to Vertex AI's evaluation models.

  • Apply hands-on knowledge of using Vertex AI's Automatic Metrics and AutoSxS for LLM evaluation.

  • Evaluate upcoming trends in generative AI evaluation, encompassing text, image, and audio models, and the importance of human evaluation.

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Assessments

3 assignments

Taught in English

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This course is part of the Harnessing LLMs: Strategy, Fine-Tuning & Evaluation Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There is 1 module in this course

This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation.

This course is ideal for AI Product Managers looking to optimize LLM applications, Data Scientists interested in advanced AI model evaluation techniques, AI Ethicists and Policy Makers focused on responsible AI deployment, and Academic Researchers studying the impact of generative AI across various domains. A basic understanding of artificial intelligence, machine learning concepts, and familiarity with natural language processing (NLP) is recommended. Prior experience with Google Cloud Vertex AI is beneficial but not required. It covers practical applications, integrating human judgment with automatic methods, and prepares learners for future trends in AI evaluation across various media, including text, images, and audio. This comprehensive approach ensures you are equipped to assess LLMs effectively, enhancing business strategies and innovation.

This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation.

What's included

12 videos4 readings3 assignments

12 videos•Total 69 minutes
  • Introduction to the Course and Meet the Instructor•3 minutes
  • Introduction to LLMs and their Evaluation Methods•6 minutes
  • Benefits and Challenges of LLM Evaluation Methods•5 minutes
  • LLM Evaluation on Vertex AI•5 minutes
  • Automatic Metrics•5 minutes
  • Automatic Metrics Demo•8 minutes
  • AutoSxS•8 minutes
  • AutoSxS Demo•8 minutes
  • Text-based Evaluation Models•6 minutes
  • Diversity Metrics and Zero-shot Evaluation for LLMs•5 minutes
  • Evaluation of Non-Text Generative AI Models•5 minutes
  • Congratulations and Continuous Learning Journey•4 minutes
4 readings•Total 20 minutes
  • Course Overview•5 minutes
  • Evaluating LLMs: A Standard Set of Metrics for Accurate Assessment•5 minutes
  • Google Generative AI Evaluation Service•5 minutes
  • Evaluating Generative AI for Image Creation•5 minutes
3 assignments•Total 45 minutes
  • Evaluating Large Language Model Outputs: A Practical Guide•20 minutes
  • Knowledge Check: Basics of Large Language Models •15 minutes
  • Knowledge Check: LLM Evaluation on Vertex AI•10 minutes

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Instructor ratings
4.8 (6 ratings)
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6 Courses•5,194 learners

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Frequently asked questions

In this course, LLM output evaluation means assessing how well a model’s responses meet the needs of a task in terms of quality, accuracy, relevance, and responsible use. The course treats it as a practical process for judging model outputs rather than simply generating them.

You would use it when choosing between models, improving an LLM-based application, or checking whether responses are accurate, fair, and appropriate for the task. It is especially useful when the output will be used in settings where reliability and judgment matter.

It fits after you know what you want the model to do and before you rely on its outputs in a real use case. In this course, evaluation helps turn model selection and refinement into a repeatable process based on goals, methods, data, and interpretation.

Checking a few responses manually can give you a quick impression, but it is often subjective and hard to repeat consistently. LLM output evaluation is more structured because it defines what good performance means and combines consistent comparison methods with human judgment when needed.

A basic understanding of artificial intelligence, machine learning concepts, and natural language processing is helpful before taking this course. No deep prior experience with Vertex AI is required, though some familiarity with it can be useful.

The course uses Google Cloud Vertex AI as the main platform for hands-on evaluation. It focuses on automatic metrics and side-by-side comparison, while also showing how human evaluation supports those methods.

You will practice defining evaluation goals, choosing evaluation methods, preparing evaluation data, comparing model outputs, and interpreting results. The course also has you work with both automated and human-centered evaluation ideas so you can assess LLM responses in a more consistent way.

Financial aid available,