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⇱ LLM Benchmarking and Evaluation Training | Coursera

LLM Benchmarking and Evaluation Training

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LLM Benchmarking and Evaluation Training

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Analyze Core LLM Capabilities: Master summarization, translation, and content generation

  • Build GenAI Applications: Create chatbots and sentiment analysis tools with LangChain

  • Evaluate LLM Performance: Use benchmarks like ROUGE, GLUE, and BIG-bench

  • Apply Real-World Use Cases: Understand industrial applications and limitations of LLMs

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments

Taught in English

Build your subject-matter expertise

This course is part of the LLM Application Engineering and Development Certification Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

This comprehensive course on Evaluating and Applying LLM Capabilities equips you with the skills to analyze, implement, and assess large language models in real-world scenarios. Begin with core capabilities, learn summarization, translation, and how LLMs power industry-relevant content generation. Progress to interactive and analytical applications—explore chatbots, virtual assistants, and sentiment analysis with hands-on demos using LangChain and ChromaDB. Conclude with benchmarking and evaluation—master frameworks like ROUGE, GLUE, SuperGLUE, and BIG-bench to measure model accuracy, relevance, and performance.

To be successful in this course, you should have a basic understanding of LLMs, Python, and NLP fundamentals. By the end of this course, you will be able to: - Explore LLM Capabilities: Understand summarization, translation, and their applications - Build LLM Applications: Create chatbots and sentiment analysis tools using real-world tools - Evaluate Model Performance: Use ROUGE, GLUE, and BIG-bench to benchmark LLMs - Analyze Use Cases: Assess benefits, limitations, and deployment of LLM-powered solutions Ideal for AI developers, ML engineers, and GenAI professionals.

Explore the core capabilities of large language models (LLMs) in this foundational module. Learn the four key functions that power LLM performance, including summarization and content translation. Understand their benefits, limitations, and real-world applications across industries. Gain hands-on experience with a text summarization demo and discover how LLMs transform content across languages.

What's included

5 videos1 reading4 assignments

5 videos•Total 38 minutes
  • Learning Objectives•2 minutes
  • Four Major Capabilities of LLM•1 minute
  • Overview, Benefits, Limitations, and Industrial Applications of Summarization•6 minutes
  • Demo: Text Summarizer•24 minutes
  • Overview, Benefits, Limitations, and Industrial Applications of Content Translation•4 minutes
1 reading•Total 10 minutes
  • Course Syllabus•10 minutes
4 assignments•Total 85 minutes
  • Quiz on Introduction to LLM Capabilities•15 minutes
  • Quiz on Introduction to Summarization•15 minutes
  • Quiz on Introduction to Content Translation•15 minutes
  • Assessment on Core Capabilities of LLMs•40 minutes

Discover how LLMs power interactive and analytical applications in this module. Learn the role of chatbots and virtual assistants in automating conversations across industries. Explore sentiment analysis to interpret user emotions and feedback. Gain hands-on experience with demos like MultiPDF QA Retriever using ChromaDB and LangChain, and real-time sentiment detection.

What's included

4 videos3 assignments

4 videos•Total 28 minutes
  • Overview, Benefits, Limitations, and Industrial Applications of Chatbots and Virtual Assistants•3 minutes
  • Demo: MultiPDF QA Retriever with ChromaDB and LangChain•12 minutes
  • Overview, Benefits, and Limitations of Sentiment Analysis•3 minutes
  • Demo: Sentiment Analysis•10 minutes
3 assignments•Total 70 minutes
  • Quiz on Chatbots and Virtual Assistants•15 minutes
  • Quiz on Introduction to Sentiment Analysis•15 minutes
  • Assessment on Interactive and Analytical LLM Applications•40 minutes

Explore how to evaluate and benchmark large language models in this comprehensive module. Learn key benchmarking steps and widely used frameworks like ROUGE, GLUE, SuperGLUE, and BIG-bench. Understand the need for evolving benchmarks as LLMs grow more advanced. Get hands-on with demos to assess performance, accuracy, and real-world application of generative AI models.

What's included

9 videos3 assignments

9 videos•Total 35 minutes
  • Benchmarking and Its Steps•4 minutes
  • Benchmarks for Language Models•1 minute
  • Demo: ROUGE Benchmark•9 minutes
  • Need for New Benchmarks•1 minute
  • GLUE Benchmark Tasks•7 minutes
  • SuperGLUE Benchmark Tasks: Part 1•7 minutes
  • SuperGLUE Benchmark Tasks: Part 2•4 minutes
  • Beyond the Imitation Game Benchmark (BIG-bench)•1 minute
  • Key Takeaways•1 minute
3 assignments•Total 70 minutes
  • Quiz on Introduction to Benchmarking•15 minutes
  • Quiz on Benchmarks for Evaluating LLMs•15 minutes
  • Assessment on LLM Evaluation and Benchmarking•40 minutes

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Frequently asked questions

LLM evaluation benchmarks are standardized tests used to assess the performance, reasoning, and language understanding of large language models. Examples include ROUGE, GLUE, SuperGLUE, and BIG-bench.

Creating a benchmark involves defining clear tasks (e.g., summarization, QA), collecting diverse datasets, selecting evaluation metrics (like F1 or accuracy), and validating the benchmark against multiple LLMs.

Common metrics include ROUGE for summarization, BLEU for translation, accuracy, F1-score, and exact match for QA tasks, along with emerging task-specific metrics for generative performance.

Benchmarks offer useful insights but may not fully reflect real-world performance. They should be used alongside practical tests, especially as models advance beyond current benchmark limits.

A structured course covering ROUGE, GLUE, SuperGLUE, and BIG-bench with hands-on demos is ideal. Look for one that combines theory, practical implementation, and real-world model assessment.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,