> ## Documentation Index
> Fetch the complete documentation index at: https://traceroot.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Run your AI system against a dataset and score every case

An **evaluation** runs your system against a dataset of cases and scores each result.

In TraceRoot every case runs as its own trace, so you inspect an eval with the same span timeline, token, and cost views you already use for tracing. Each scorer emits a metric, and you read a run per metric — there is no single headline score.

The SDK is available in **Python** and **TypeScript**, with the same API in both.

## What you write

* A **dataset** — the cases to run, each with an `input` and an optional `expected` answer.
* A **task** — your system under evaluation, mapping an `input` to an `output`.
* One or more **scorers** — functions that turn a case's result into a metric.

Then `evaluate()` runs the task over every case, scores each result, and reports the run.

## Where to start

<CardGroup cols={2}>
  <Card title="Get Started" icon="rocket" href="/docs/evals/get-started">
    Author a dataset, write a scorer, and run your first eval.
  </Card>

  <Card title="Datasets" icon="table" href="/docs/evals/datasets">
    Author, version, and publish the cases you run against.
  </Card>

  <Card title="Scorers" icon="ruler" href="/docs/evals/scorers">
    Code scorers and LLM-as-a-judge.
  </Card>

  <Card title="Running Evals" icon="play" href="/docs/evals/running-evals">
    <code>evaluate()</code> — the task, the scorers, and where a run reports.
  </Card>

  <Card title="Reading Results" icon="list-check" href="/docs/evals/reading-results">
    Per-metric summaries and per-case results.
  </Card>
</CardGroup>