A complete guide
to LLM quality

How to assess the quality of LLM outputs and generative systems?

Large Language Models (LLMs) are behind many popular applications today, from chatbots and code generators to healthcare assistants. Ensuring these systems produce high-quality outputs while minimizing risks is critical. 

But evaluating LLM performance can be tricky. The responses are open-ended, there are multiple "right" answers, and what counts as "quality" can be subjective—things like tone or style play a role. Plus, LLMs can now engage in multi-turn conversations and autonomous workflows, making the evaluation process even more complex.

This guide breaks down the key ideas for assessing LLM system quality both offline (before deployment) and online (real-time monitoring). You'll also learn about key ideas like LLM tracing and observability.

What you will find in this guide:

• How to evaluate the quality of LLM-powered products. We cover the basics of assessing LLM outputs, testing prompts, and measuring system performance.
• Explainers for specific methods and metrics. We explore select approaches in-depth, from semantic similarity to using LLMs to judge their own outputs.
• Beginner-friendly explainers. Each topic is explained in simple terms, so you don't need deep technical knowledge or prior experience with machine learning.
• Plenty of visuals. All explanations come with illustrations to make complex ideas easier to understand.
• Modular format. Each section is standalone, so you can jump into specific topics without needing to follow the guide from start to finish.

The goal of this guide is to provide a beginner-friendly resource to help anyone working with LLM systems effectively evaluate their quality, and build reliable, performant, and safe AI products.