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()

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

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",
});