Evaluate LLMs Effectively Using DeepEval: A Practical Guide

Effectively evaluating Large Language Models (LLMs) is crucial given their rapid advancement. Existing machine learning evaluation frameworks often fall short in comprehensively testing LLMs across diverse properties. DeepEval offers a robust solution, providing a multi-faceted evaluation framework that assesses LLMs on accuracy, reasoning, coherence, and ethical considerations.

This tutorial provides a practical guide to DeepEval, demonstrating how to create a relevance test (akin to Pytest) and utilize the G-eval metric. We'll also benchmark the Qwen 2.5 model using MMLU. This beginner-friendly tutorial is designed for those with a technical background seeking a better understanding of the DeepEval ecosystem.

For those new to LLMs, a foundational understanding can be gained through the Master Large Language Models (LLMs) Concepts course.

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