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

# LLM evaluations

LLM evaluations are the offline testing layer for your prompts and LLM pipelines. Define inputs, run them through the version you want to test, score the outputs against target data, and compare across runs to see if anything regressed. To evaluate an agent end to end in a working copy of its environment, see [Agent evaluations](https://tracevell.com/docs/agent-evaluations/introduction.md).

> [card] [Introduction](https://tracevell.com/docs/evaluations/introduction.md)
>
> What evaluations are and when to use them.

> [card] [Quickstart](https://tracevell.com/docs/evaluations/quickstart.md)
>
> Write and run your first evaluation in five minutes.

> [card] [Concepts](https://tracevell.com/docs/evaluations/concepts.md)
>
> Datapoints, executors, evaluators, and how they map to traces.

> [card] [Compare runs](https://tracevell.com/docs/evaluations/comparing-runs.md)
>
> Groups, the progression chart, and side-by-side comparison.

> [card] [Datasets](https://tracevell.com/docs/evaluations/datasets.md)
>
> Back your evaluation with a Tracevell dataset instead of hardcoded data.

> [card] [Manual API](https://tracevell.com/docs/evaluations/manual-evaluation.md)
>
> Lower-level control for pipelines where `evaluate()` is too opinionated.

> [card] [Self-hosted](https://tracevell.com/docs/evaluations/self-hosted.md)
>
> Point the SDK at a self-hosted Tracevell instance.

Next: turn failing traces into reusable evaluation data with [Datasets](https://tracevell.com/docs/datasets/introduction.md), or catch regressions in production with [Signals](https://tracevell.com/docs/signals/introduction.md).


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