> ## 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.

# TypeScript

> Submit completion feedback from TypeScript to power prompt optimization

## Completion Feedback

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

### `sendFeedback()`

```typescript theme={null}
await ze.sendFeedback({
  promptSlug: "support-bot",
  completionId: "550e8400-e29b-41d4-a716-446655440000",
  thumbsUp: false,
  reason: "Response was too verbose",
  expectedOutput: "A concise 2-3 sentence response",
});
```

| Parameter        | Type                      | Required | Description                                      |
| ---------------- | ------------------------- | -------- | ------------------------------------------------ |
| `promptSlug`     | `string`                  | Yes      | Prompt name (same as `ze.prompt({ name: ... })`) |
| `completionId`   | `string`                  | Yes      | UUID of the completion span                      |
| `thumbsUp`       | `boolean`                 | Yes      | Positive or negative feedback                    |
| `reason`         | `string`                  | No       | Explanation of the feedback                      |
| `expectedOutput` | `string`                  | No       | What the output should have been                 |
| `metadata`       | `Record<string, unknown>` | No       | Additional metadata                              |
| `judgeId`        | `string`                  | No       | Judge automation ID (for judge feedback)         |
| `expectedScore`  | `number`                  | No       | Expected score (for scored judges)               |
| `scoreDirection` | `'too_high' \| 'too_low'` | No       | Score direction for scored judges                |

### End-to-end example

```typescript theme={null}
import * as ze from "zeroeval";
import { OpenAI } from "openai";

ze.init();
const client = ze.wrap(new OpenAI());

const systemPrompt = await ze.prompt({
  name: "support-bot",
  content: "You are a helpful customer support agent.",
});

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

const isGood = evaluateResponse(response.choices[0].message.content);

await ze.sendFeedback({
  promptSlug: "support-bot",
  completionId: response.id,
  thumbsUp: isGood,
  reason: isGood ? "Clear instructions" : "Missing reset link",
});
```