> ## Documentation Index > Fetch the complete documentation index at: https://docs.coval.ai/llms.txt > Use this file to discover all available pages before exploring further. # Add your first metric > Create a metric and attach it to a run, end to end. A metric only does something once it's attached to a run. There are two ways to get started: use one of Coval's built-in metrics, or create your own. ## Use a built-in metric Every run lets you pick which metrics to track — you don't have to create anything first. From the **Launch Simulations** page, start a run as usual (agent, persona, test set). See [Simulations](/concepts/simulations/overview#launching-a-simulation). In the launch panel, pick one or more built-in metrics — for example **Latency**, **End Reason**, or **Transcript Sentiment**. Not sure which? See [Choose a metric](/concepts/metrics/choose-a-metric). When the run finishes, open it from [Runs](/concepts/runs/overview). Each simulation shows a metric card with its score; click into a simulation to see the score explained against the transcript. ## Create your own metric When the built-ins don't capture your exact success criteria, author a custom metric. The most common starting point is a **Binary LLM Judge** — a yes/no question about the transcript. Go to **Metrics** in the sidebar and click **New Metric**. Choose the type that fits what you're measuring. For a yes/no check, pick **Binary LLM Judge**. Browse all types in the [Metric Library](/concepts/metrics/overview#the-metric-library). Give the metric a name and supply what that type needs — a prompt for an LLM judge, a pattern for a regex check, or a threshold for a numeric metric. For prompts, follow [Write judge prompts](/concepts/metrics/writing-judge-prompts). ``` Given the transcript, did the assistant confirm the appointment date and time before ending the call? Return YES if the assistant restated both the date and the time and the user acknowledged them. Return NO otherwise. ``` Open the metric and click **Improve Metric** to run it against real transcripts and check how often it returns YES vs. NO. Tighten the wording until results are consistent. The strongest signal here is [human review](/concepts/metrics/human-review/human-review). Save the metric, then select it when you launch your next evaluation — exactly like a built-in. Its scores appear alongside the others in the run results. ## Where to go next Find the right metric for what you want to measure. Make your LLM-judge metrics reliable. Template variables, transcript scope, trace context, thresholds. Every metric type, in depth.