> ## Documentation Index > Fetch the complete documentation index at: https://docs.befailproof.ai/llms.txt > Use this file to discover all available pages before exploring further. # Jev evaluations > Use Jev to score a finished session against a question with known answers. A Jev evaluation reads a **finished session** and gives a score from 0 to 1. Use it when the answer is known in advance, such as “Did the customer express urgency?” or “How frustrated was the customer?” It helps you find patterns across runs; it does not stop a tool call. For decisions made **before** a tool runs, use [Jev policies](/policies/jev). ## Create one in the dashboard 1. Open **Analyze → eval authoring** and select **new eval**. 2. Describe one question and its possible answers. For example: “Did the agent promise a refund before checking the refund policy? Answer yes or no.” Select **draft** and review that the result is a classifier score. 3. [Test it](/evaluations/test) on recent sessions, then [deploy it](/evaluations/deploy). New completed sessions are scored; [backfill](/evaluations/deploy#score-sessions-you-already-have) if you also need history. The shared eval authoring form, where you describe a fixed-answer question, review the draft, and deploy after testing. The example shown is a code evaluation; a Jev question uses the same authoring flow. The assistant can choose between code, Jev classification, and a [judge](/evaluations/judge). Check its choice before deploying. Jev gives a score without prose reasoning; choose a judge when you need an explanation. See the [Jev evaluation reference](/reference/jev-evaluations) for question types and score limits. ## Read the scores Open **Observe → Evaluations** to chart the result by agent and time. From a terminal, the Cloud CLI can read the same results: ```bash theme={null} fp evals --since 7d fp evals --aggregate --since 7d ``` The Cloud CLI reads results; authoring and deployment happen in the dashboard. See the [Cloud CLI reference](/reference/cloud-cli#evaluations) for filters. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.