LLM Evaluation Metrics & Benchmarks
Explore various metrics and benchmarks for quantitatively assessing the quality, relevance, and accuracy of LLM outputs.
Intro to LLM Evaluation
Welcome! As developers working with Large Language Models (LLMs), it's crucial to know how to measure their performance. But how do we objectively say one LLM output is 'better' than another?
This lesson explores various metrics and benchmarks used to quantitatively assess the quality, relevance, and accuracy of LLM outputs.
Why Evaluate LLM Outputs?
LLMs are powerful, but they can sometimes:
- Hallucinate: Make up facts or provide incorrect information.
- Exhibit Bias: Reflect biases present in their training data.
- Be Inconsistent: Give different answers to similar prompts.
- Lack Relevance: Provide outputs that don't directly answer the prompt.
Evaluation helps us identify these issues, track improvements, and ensure our LLM applications are reliable.
All lessons in this course
- LLM Evaluation Metrics & Benchmarks
- Human-in-the-Loop Feedback Systems
- Prompt Injection & Security Best Practices
- Detecting & Mitigating Hallucinations