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Metrics page overviewMetrics page overview
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.
1

Open Metrics and explore templates

Go to your project’s Metrics page. Start fast by copying a proven template, then tailor the guidelines to your domain.
Metric templates listMetric templates list
2

Create a metric

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.
AI metric detail with model and output type settingsAI metric detail with model and output type settings
3

Go to the Records page and select records

Navigate to your project’s Records page. Select the records you want to score, then click the Score Records button.
Records page showing selected records and Score Records buttonRecords page showing selected records and Score Records button
4

Choose metrics and score

In the Score Records modal, select one or more metrics to evaluate against, then click Score.
Score Records modal with metrics selectedScore Records modal with metrics selected
5

View scores in the record panel

Once scoring completes, click any record to open the side panel. View scores, inputs, outputs, and evaluation details.
Record side panel showing metric scores, inputs, and outputsRecord side panel showing metric scores, inputs, and outputs

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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).

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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.

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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.

Runs

Create and analyze evaluations

A/B Comparison

Compare two runs side‑by‑side

Best‑in‑Class Metrics

Explore curated, proven metrics

API Reference

Create metrics via API