LLM Benchmarking and Evaluation Training

Overview

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.

Syllabus

Core Capabilities of LLMs

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.

Interactive and Analytical LLM Applications

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.

LLM Evaluation and Benchmarking

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.