User Feedback

User feedback measures whether your AI actually helped users. Use it to find quality issues, build better evaluation datasets, and prioritize improvements based on real user experiences. In Langfuse, feedback is captured as scores and linked to traces.

Feedback Types

Explicit Feedback

Users directly rate responses through thumbs up/down, star ratings, or comments.

Implicit Feedback

Derived from user behavior like time spent reading, copying output, accepting suggestions, or retrying queries.

Both work as scores in Langfuse. Filter traces by score, build annotation queues, or use feedback as ground truth for automated evaluations.

Quick Start

This example shows how to collect explicit user feedback from a chatbot built with Next.js and AI SDK. You can find the full implementation in the Langfuse Example repository.

1. Return trace ID to frontend

Your backend sends the trace ID so frontend can link feedback to the trace.

// app/api/chat/route.ts
import { getActiveTraceId } from "@Langfuse/tracing";
 
export const POST = observe(async (req: Request) => {
  const result = streamText({
    model: openai('gpt-4o-mini'),
    messages: convertToModelMessages(messages),
  });
  return result.toUIMessageStreamResponse({
    generateMessageId: () => getActiveTraceId() || "",
  });
});

2. Collect feedback in frontend

Use Langfuse Web SDK to send feedback as a score.

import { LangfuseWeb } from "langfuse";
 
const langfuse = new LangfuseWeb({
  publicKey: process.env.NEXT_PUBLIC_LANGFUSE_PUBLIC_KEY,
  baseUrl: process.env.NEXT_PUBLIC_LANGFUSE_HOST,
});
function FeedbackButtons({ messageId }: { messageId: string }) {
  const handleFeedback = (value: number, comment?: string) => {
    langfuse.score({
      traceId: messageId,
      name: "user-feedback",
      value: value, // 1 for positive, 0 for negative
      comment: comment,
    });
  };
  return (
    <div>
      <button onClick={() => handleFeedback(1)}>:+1:</button>
      <button onClick={() => handleFeedback(0)}>:-1:</button>
    </div>
  );
}

3. View feedback in Langfuse

Feedback appears as scores on traces. You can filter by user-feedback < 1 to find low-rated responses.

Server-side Feedback

Record feedback from your backend when needed, such as after a user survey or follow-up interaction. You could also use this to log implicit feedback signals such as ticket closures or successful task completions.

from langfuse import get_client
langfuse = get_client()
 
# Check if customer support ticket was resolved successfully
ticket_status = checkIfTicketClosed(ticket_id="ticket-456")
if ticket_status.is_closed:
    langfuse.create_score(
        trace_id=ticket_status.trace_id,
        name="ticket-resolution",
        value=1,
        comment=f"Ticket closed successfully after {ticket_status.resolution_time}"
    )
else:
    langfuse.create_score(
        trace_id=ticket_status.trace_id,
        name="ticket-resolution",
        value=0,
        comment=f"Ticket escalated to human agent"
    )

Implicit Feedback with LLM-as-a-Judge

Automatically evaluate every response for qualities like user sentiment, satisfaction, or engagement using LLMs as judges. This lets you gather large-scale feedback without user intervention.

See LLM-as-a-Judge Evaluators for implementation patterns and examples.

Example App

The user-feedback example shows a complete Next.js implementation with:

• OpenTelemetry tracing
• Thumbs up/down with optional comments
• Session tracking across conversations