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Educational Guide

LLM Evaluation Metrics: How to Measure AI Quality

Comprehensive guide to LLM evaluation metrics. Learn how to measure accuracy, relevance, and quality in AI outputs.

Definition

LLM evaluation metrics are quantitative and qualitative measures used to assess the quality, accuracy, and usefulness of large language model outputs.

Types of Evaluation Metrics

Common metric categories: 1. **Accuracy metrics**: Correctness of factual claims 2. **Relevance metrics**: How well outputs match the query 3. **Coherence metrics**: Logical flow and readability 4. **Safety metrics**: Harmful content detection 5. **Format metrics**: Adherence to output structure

Automated Metrics

Machine-computed metrics: - **BLEU/ROUGE**: Text similarity to reference outputs - **Perplexity**: Model confidence in outputs - **Semantic similarity**: Embedding-based comparison - **Exact match**: Character-for-character comparison - **F1 Score**: Precision and recall balance

Human Evaluation

Human judgment metrics: - **Helpfulness ratings**: Did the response help? - **Preference comparisons**: A vs B testing - **Error annotation**: Flagging specific issues - **Quality rubrics**: Structured scoring guides

Custom Metrics for Your Use Case

Build metrics specific to your needs: - Citation accuracy for RAG systems - Code correctness for coding assistants - Tone consistency for customer support - Safety scores for sensitive applications

Related Topics

Quality Scoring

Quality scoring is the process of systematically evaluating LLM outputs against defined criteria to produce numerical scores that enable comparison and threshold-based decisions.

Prompt Testing Best Practices

A collection of proven methods and workflows for systematically testing LLM prompts to ensure quality, reliability, and safety in production applications.

LLM Output Validation

LLM output validation is the process of checking model outputs for correctness, safety, format compliance, and quality before presenting them to users or using them in downstream systems.

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