Log user feedback

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How it works

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Capture feedback via SDK

// When the user gives a thumbs up/down
async function handleUserFeedback(
  traceId: string,
  feedback: "positive" | "negative",
  comment?: string
) {
  // Retrieve or store the trace reference from your application
  const trace = getStoredTrace(traceId);

  trace.update({
    attributes: {
      "user_feedback": feedback,
      "user_feedback_comment": comment || "",
      "user_feedback_timestamp": new Date().toISOString(),
    },
    tags: [feedback === "positive" ? "thumbs-up" : "thumbs-down"],
  });
}

async def handle_user_feedback(
    trace_id: str,
    feedback: str,  # "positive" or "negative"
    comment: str = "",
):
    trace = get_stored_trace(trace_id)

    trace.update(
        attributes={
            "user_feedback": feedback,
            "user_feedback_comment": comment,
            "user_feedback_timestamp": datetime.now().isoformat(),
        },
        tags=["thumbs-up" if feedback == "positive" else "thumbs-down"],
    )

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Capture feedback via API

PATCH /v2/logs/trace
Authorization: Bearer your-api-key
Content-Type: application/json

{
  "traceId": "your-trace-id",
  "projectId": "your-project-id",
  "attributes": {
    "user_feedback": "negative",
    "user_feedback_comment": "Response was too vague",
    "user_feedback_rating": 2
  },
  "tags": ["thumbs-down", "needs-improvement"]
}

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Capture feedback via Proxy

# Use the same trace reference ID as the original request
headers["adaline-trace-reference-id"] = original_trace_id
headers["adaline-trace-attributes"] = json.dumps([
    {"operation": "create", "key": "user_feedback", "value": "negative"},
    {"operation": "create", "key": "user_feedback_comment", "value": "Too vague"},
])
headers["adaline-trace-tags"] = json.dumps([
    {"operation": "create", "tag": "thumbs-down"},
])

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Feedback data patterns

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Use feedback for improvement

1. Filter in Monitor — Filter traces by feedback attributes to find cases where users were dissatisfied.
2. Build datasets — Add negative feedback cases to evaluation datasets so you can test fixes against real user complaints. In Monitor, open a trace, click Add to Dataset, and map feedback attributes into dataset columns.
3. Annotation queues — Use an annotation_status column in your dataset to track which rows need human review. Reviewers fill in corrective annotations, then mark rows as filled. This gives you a structured backlog with clear ownership.
4. Run evaluations — Attach evaluators to the dataset to verify that prompt fixes actually resolve the failure class. Run on annotated rows first, then the full dataset. Previously failed rows stay in the dataset permanently as regression guardrails.
5. Track trends — Use charts with custom attributes to monitor satisfaction trends over time.
6. Correlate with eval scores — Compare user feedback against continuous evaluation scores to validate your evaluators.

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Best practices

• Capture immediately — Send feedback as soon as the user provides it, even if the trace has already ended.
• Use tags for quick filtering — Tags like "thumbs-up" and "thumbs-down" make it easy to filter in the Monitor without complex attribute queries.
• Include context — When possible, capture the reason for negative feedback (comment or category) to make it actionable.
• Link to the right trace — Store the trace ID in your application when you display a response, so you can attach feedback to the correct trace later.

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Next steps