> ## 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.

# Python

> Submit completion feedback from Python to power prompt optimization

## Completion Feedback

Attach structured feedback to a specific LLM completion to power prompt optimization.

### `send_feedback()`

```python theme={null}
ze.send_feedback(
    prompt_slug="support-bot",
    completion_id="550e8400-e29b-41d4-a716-446655440000",
    thumbs_up=False,
    reason="Response was too verbose",
    expected_output="A concise 2-3 sentence response"
)
```

| Parameter           | Type    | Required | Description                                                                       |
| ------------------- | ------- | -------- | --------------------------------------------------------------------------------- |
| `prompt_slug`       | `str`   | Yes      | Prompt name (same as `ze.prompt(name=...)`)                                       |
| `completion_id`     | `str`   | Yes      | UUID of the completion span                                                       |
| `thumbs_up`         | `bool`  | Yes      | Positive or negative feedback                                                     |
| `reason`            | `str`   | No       | Explanation of the feedback                                                       |
| `expected_output`   | `str`   | No       | What the output should have been                                                  |
| `metadata`          | `dict`  | No       | Additional metadata                                                               |
| `judge_id`          | `str`   | No       | Judge automation ID (for judge feedback)                                          |
| `expected_score`    | `float` | No       | Expected score (for scored judges)                                                |
| `score_direction`   | `str`   | No       | `"too_high"` or `"too_low"`                                                       |
| `criteria_feedback` | `dict`  | No       | Per-criterion feedback: `{"criterion": {"expected_score": 4.0, "reason": "..."}}` |

### End-to-end example

```python theme={null}
import zeroeval as ze
from openai import OpenAI

ze.init()
client = OpenAI()

system_prompt = ze.prompt(
    name="support-bot",
    content="You are a helpful customer support agent."
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "How do I reset my password?"}
    ]
)

is_good = evaluate_response(response.choices[0].message.content)

ze.send_feedback(
    prompt_slug="support-bot",
    completion_id=response.id,
    thumbs_up=is_good,
    reason="Clear instructions" if is_good else "Missing reset link"
)
```