> ## Documentation Index
> Fetch the complete documentation index at: https://docs.scorecard.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Metrics Quickstart
> Create metrics, group them, run evaluations, and read scores.
export const DarkLightImage = ({lightSrc, caption, alt, darkSrc = null, width = "1000"}) => {
const getAbsoluteUrl = src => {
if (src.startsWith('http://') || src.startsWith('https://')) {
return src;
}
const currentUrl = typeof window !== 'undefined' ? window.location.origin : '';
if (currentUrl.includes('.mintlify.app')) {
const subdomain = currentUrl.split('.')[0].replace('https://', '');
return `https://mintlify.s3.us-west-1.amazonaws.com/${subdomain}${src.startsWith('/') ? '' : '/'}${src}`;
} else if (currentUrl === 'https://docs.scorecard.io') {
return `https://mintlify.s3.us-west-1.amazonaws.com/scorecard-d65b5e8a${src.startsWith('/') ? '' : '/'}${src}`;
} else {
return `${currentUrl}${src.startsWith('/') ? '' : '/'}${src}`;
}
};
const content = <>
})
;
if (caption) {
return {content};
} else {
return content;
}
};
Metrics define what “good” looks like for your LLM. You describe the criteria (e.g., helpfulness, groundedness, safety), and Scorecard turns that into repeatable scores you can track across runs and over time.
Go to your project’s **Metrics** page. Start fast by copying a proven template, then tailor the guidelines to your domain.
You can also create a metric from scratch. Provide a name, description, clear guidelines, and choose an Evaluation Type and Output Type.
Guidelines matter. Describe what to reward and what to penalize, and include 1–2 concise examples if helpful. These instructions become the core of the evaluator prompt.
Uses a model to apply your guidelines consistently and at scale. Pick the evaluator model and keep temperature low for repeatability.
Best for nuanced judgments or gold‑standard baselines. Select Human as the evaluation type and write clear instructions for reviewers.
Deterministic, code‑based checks (e.g., latency, regex, policy flags). Select Heuristic (SDK) as the evaluation type and provide a scorer function in Python or TypeScript.
An agentic evaluator that can use tools, browse context, and reason over multiple steps before producing a score. Stay tuned for updates.
Navigate to your project's [Records page](/features/records). Select the records you want to score, then click the **Score Records** button.
In the **Score Records** modal, select one or more metrics to evaluate against, then click **Score**.
Once scoring completes, click any record to open the side panel. View scores, inputs, outputs, and evaluation details.
## Metric types
* **AI‑scored**: Uses a model to apply your guidelines consistently and at scale.
* **Human‑scored**: Great for nuanced judgments or gold‑standard baselines.
* **Heuristic (SDK)**: Deterministic, code‑based checks via the SDK (e.g., latency, regex, policy flags).
* **Critic Agent** *(coming soon)*: An agentic evaluator that reasons over multiple steps with tool use.
* **Output types**: Choose Boolean (pass/fail) or Integer (1–5).
## Second‑party metrics (optional)
If you already use established evaluation libraries, you can mirror those metrics in Scorecard:
* **MLflow genai**: Relevance, Answer Relevance, Faithfulness, Answer Correctness, Answer Similarity
* **RAGAS**: Faithfulness, Answer Relevancy, Context Recall, Context Precision, Context Relevancy, Answer Semantic Similarity
Copy a matching template, then tailor the guidelines to your product domain.
## Best practices for strong metrics
* **Be specific.** Minimize ambiguity in guidelines; include “what not to do.”
* **Pick the right output type.** Use Boolean for hard requirements; 1–5 for nuance.
* **Keep temperature low.** Use ≈0 for repeatable AI scoring.
* **Pilot and tighten.** Run on 10–20 cases, then refine wording to reduce false positives.
* **Bundle into groups.** Combine complementary checks (e.g., Relevance + Faithfulness + Safety) to keep evaluations consistent.
Looking for vetted, ready‑to‑use metrics? Explore Best‑in‑Class Metrics and copy templates (including MLflow and RAGAS). You can also create deterministic checks via the SDK using Heuristic metrics.
## Related resources
Create and analyze evaluations
Compare two runs side‑by‑side
Explore curated, proven metrics
Create metrics via API