LLM evaluation metrics

What are LLM evaluation metrics?

LLM evaluation metrics are performance indicators used to judge the effectiveness of LLMs. Unlike traditional ML metrics like accuracy or F1, LLM evaluations often rely on open-ended responses that require subjective or rubric-based scoring.

They may assess aspects such as:

Correctness or factuality
Relevance to the prompt
Readability and fluency
Safety and bias avoidance

Why it matters in AI/ML

Without structured evaluation, GenAI teams risk:

Shipping models that hallucinate or mislead users
Overlooking biases or offensive outputs
Deploying updates that regress performance

Robust metrics enable comparison across prompt versions, model updates, and even different LLM providers.

Common LLM evaluation methods

1. LLM-as-a-judge

Use another LLM (or the same one) to rate outputs against criteria (e.g., coherence, helpfulness, correctness)
Enables scalable evaluation with consistent grading

2. Human Annotation

Manual review using rubrics or Likert-scale scoring
Best for safety, tone, or brand alignment

3. Behavioral Testing

Design prompt sets to test behavior under specific conditions (e.g., adversarial prompts, edge cases)

4. Automated Text Similarity Metrics

BLEU, ROUGE, METEOR: useful for summarization or translation tasks, though limited for creativity or reasoning

5. Custom Rubrics

Define criteria based on business goals (e.g., does the answer align with internal knowledge base?)

Challenges

Subjectivity: “good” output varies by task and user expectations
Scalability: human reviews are expensive and slow
LLM-based grading may inherit bias or inconsistency

Explore these terms to understand how LLM evaluation connects to testing, tracing, and production readiness.