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.
LLM Benchmarking and Evaluation Training
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LLM Benchmarking and Evaluation Training
This course is part of LLM Application Engineering and Development Certification Specialization
Instructor: Priyanka Mehta
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
5 hours to complete
3 weeks at 1 hour a week
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
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
July 2025
Assessments
10 assignments
Taught in English
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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
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
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
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
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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.
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