Evaluating large language models (LLMs) is an emerging discipline that sits somewhere between data science, linguistics, and system reliability. When you build systems around LLMs, calling the API and hoping for good output isn’t enough — you need a way to measure quality, detect regressions, and reason about improvements over time.
This post summarizes what I’ve learned while exploring current research and practices in LLM evaluation: common metrics, trade-offs between different approaches, and how teams typically start building simple evaluation pipelines.
What Are LLM Evaluation Metrics
LLM evaluation metrics are methods to assign a measurable score to model outputs — to help determine whether a new model, prompt, or retrieval setup performs better than the previous one.
Metrics generally aim to capture dimensions such as:
- Correctness / factuality — are statements accurate?
- Relevance — does the response actually answer the prompt?
- Hallucination — does the model invent unsupported claims?
- Task completion — did it perform the requested action?
- Safety — does it avoid toxic or biased language?
The challenge is that LLM outputs are probabilistic and context-dependent. Unlike classical ML models, there’s often no single “correct” answer.
Two Broad Approaches to Evaluation
Most current work falls into two families of evaluators:
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Traditional / statistical metrics
Examples include BLEU, ROUGE, edit distance, and METEOR. These compare generated text against reference outputs.- Advantages: fast, reproducible, inexpensive.
- Drawbacks: poor at judging meaning or reasoning; easily misled by rewording.
-
Model-based or “LLM-as-judge” approaches
These use another model to rate or rank outputs — sometimes the same LLM class, sometimes a smaller one fine-tuned for evaluation.- Advantages: better semantic understanding, closer to human judgment.
- Drawbacks: can be inconsistent, costly, and biased; require careful prompt design.
In practice, hybrid systems are becoming common: traditional metrics for structure or lexical similarity, model-based scoring for nuance and factuality.
Common Metrics in Use
A few dimensions appear repeatedly across papers and open-source projects:
- Answer relevancy — does the output address the prompt meaningfully?
- Factual correctness — are the statements true given reference data?
- Hallucination rate — how often does the model produce unsupported claims?
- Task completion — did it follow instructions fully?
- Context faithfulness (for RAG systems) — how well does the answer align with retrieved documents?
- Safety — checks for bias, toxicity, or privacy issues.
- Custom or domain-specific metrics — e.g., summary accuracy, tone alignment, or tool-use correctness.
Even in research settings, teams rarely use all of these at once. A handful of well-chosen metrics can be far more informative than an exhaustive list.
Getting Started: Practical First Steps
For teams experimenting with LLMs, the most effective starting point seems to be lightweight and automated:
- Define what “good” means — a short list of success criteria (e.g., factual, relevant, safe).
- Collect a small evaluation set — 20–50 representative prompts and ideal outputs.
- Start with simple checks — string similarity or embedding distance for relevance; a small LLM as a factuality or toxicity judge.
- Automate the loop — run evaluations automatically on every prompt or model change.
- Track results and thresholds — log scores, compare versions, and block regressions.
The goal isn’t perfect measurement — it’s consistent measurement that helps you notice when things get worse.
Reflections
LLM evaluation is still a developing field. Many of the metrics in use are experimental, and even “LLM-as-judge” methods can disagree with human reviewers. What seems most effective today is combining quantitative metrics with small, high-quality human review sets and tracking trends over time rather than single scores.
For anyone getting into the space, the best approach is iterative: start small, automate what you can, and treat evaluation as part of the engineering feedback loop rather than a one-off exercise.