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

# Introduction

> Attach human and AI feedback to your agent interactions to drive quality improvements

Your agent produces thousands of outputs, but without feedback you can't tell which ones are good. Feedback closes the loop -- it connects real-world quality judgments to the traces, spans, and completions your agent generates.

ZeroEval supports two kinds of feedback:

* **Human feedback** -- thumbs-up/down, star ratings, corrections, and expected outputs submitted by users or reviewers
* **AI feedback** -- automated evaluations from calibrated judges that score outputs against criteria you define

Both feed into the same system. Feedback attached to completions powers [prompt optimization](/autotune/introduction). You can also retrieve unified feedback -- combining human reviews and judge evaluations -- for any span, trace, or session via the [Feedback API](/feedback/api-reference#unified-entity-feedback).

## How feedback flows

<Steps>
  <Step title="Agent produces output">
    Your agent runs and ZeroEval captures the full trace -- inputs, outputs,
    model, prompt version.
  </Step>

  <Step title="Feedback is attached">
    Humans review outputs in the dashboard or your app submits feedback
    programmatically. Judges evaluate outputs automatically based on your
    criteria.
  </Step>

  <Step title="Quality becomes measurable">
    Feedback appears on spans, traces, and completions in the console. Filter by
    thumbs-up rate, judge scores, or tags to find patterns.
  </Step>

  <Step title="Improvements are driven by data">
    Use feedback to optimize prompts, compare models, calibrate judges, and
    catch regressions before users do.
  </Step>
</Steps>

## Get started

<CardGroup cols={2}>
  <Card title="Human Feedback" icon="user" href="/feedback/human-feedback">
    Collect feedback from reviewers in the dashboard or from end users via
    like/dislike buttons, ratings, and corrections.
  </Card>

  <Card title="AI Feedback (Judges)" icon="scale-balanced" href="/judges/introduction">
    Configure AI evaluators that automatically score every completion against
    criteria you define. Includes built-in judges for common failures.
  </Card>
</CardGroup>