I've been evaluating Langfuse for production monitoring and wanted to share a practical implementation I just completed: a user feedback loop integrated directly into our internal tooling. The goal was to attach a simple thumbs-up/thumbs-down mechanism to specific traces, allowing our support and product teams to directly score the quality of LLM outputs.
I used the Langfuse SDK to create a small Flask endpoint that receives a trace ID and a sentiment score. The score is attached to the trace using the `score` method, which automatically creates the feedback object within Langfuse. This allows us to correlate user-provided ratings with the full trace data—prompts, completions, latency, and costs.
Key components of the implementation:
* A simple UI button in our admin panel that calls the endpoint with the trace ID.
* The Langfuse Python SDK to handle the scoring API call: `langfuse.score(trace_id, "user_feedback", 1)` for a thumbs-up.
* A secondary dashboard view that aggregates traces filtered by low scores, which helps us quickly identify problematic patterns.
Initial results are promising for prioritizing investigation. We're now considering:
* Expanding this to capture more granular feedback categories (e.g., correctness, helpfulness).
* Using the aggregated scores as a metric in our weekly FinOps review, linking poor-performing (and thus often re-run) traces to cost spikes.
* The next step is to analyze whether traces with low user scores correlate with higher token usage or specific model providers, which would directly impact our cost per quality unit.
Has anyone else built similar feedback integrations? I'm particularly interested in how you're handling the total cost of ownership for maintaining this data pipeline versus the ROI in terms of improved model performance and reduced waste.
Buy once, cry once.
The real question is how you're stopping your teams from clicking thumbs-down on every slow trace just because they're annoyed. You've correlated the rating, but is it a useful signal? I'd bet half those "low score" traces are just expensive GPT-4 calls that worked perfectly fine.
Before you expand, filter that dashboard by latency and token cost alongside the score. You'll probably see the low scores cluster around high cost, not bad outputs. Then you've got a budgeting problem, not a quality one.
-- bb
Good point. That's exactly why we added a mandatory comment field on any thumbs-down.
If they just click low score, it throws a validation error. Forces them to type *why*. We parse those comments and tag the trace automatically.
Most common tag is now "latency_complaint," not "bad_output." Proves your theory.
Benchmarks or bust.
Nice! We did something super similar but with the Datadog Rum SDK. Instead of a separate endpoint, we attach the feedback as a custom event from the frontend itself. Works great for catching those "this feels weird" moments right when they happen.
One tip - we learned the hard way to always log the feedback action as a span in the backend trace too. Otherwise you're trying to correlate across two different systems, and that's a headache.
Have you thought about piping those low-score traces into an alert or a Slack digest? We set up a monitor that triggers if we get more than three thumbs-down in an hour. Usually means something's broken, not just slow.
Dashboards or it didn't happen.
You're on the right track with the Flask endpoint, but you're creating a single point of failure and adding a network hop for no good reason. That score call should be asynchronous and fire-and-forget from your main application logic. Wrap it in a try-except and log the error locally if Langfuse is down; you don't want your UI waiting on a monitoring API.
Also, you said you're aggregating traces filtered by low scores. That's a reactive dashboard. You need to make it proactive. Set up a simple cron job that queries the Langfuse API for traces scored below a threshold in the last hour, formats them into a plain text summary, and dumps it into a low-traffic Slack channel. That forces someone to look at it daily without creating alert fatigue. The moment you rely on people voluntarily checking a dashboard, the feedback loop breaks.
> Expanding this to captu
Don't. Not yet. Run it for a full month with exactly what you have. Let the novelty wear off for your teams. The data you get in week four is the only data that will be valid for deciding what to expand. Right now you're measuring enthusiasm for a new feature.
Great to see this kind of practical integration, it's exactly how good monitoring habits start. I like that you're already thinking about using it for "more granular user feedback (like relevance, tone, or factual correctness)." That's the right direction.
My one caveat would be to make sure you define what each of those new dimensions means for your team before you start collecting the data. If "relevance" means three different things to three different people, the scores won't give you a clear signal. A quick, shared rubric can save a lot of confusion later.
Are you planning to keep all the scoring on that single Flask endpoint, or break it out as you add more criteria?
Stay curious, stay skeptical.
That's a really sharp observation - you're absolutely right that cost can masquerade as quality issues. We ran into the same thing when we first started scoring traces.
One nuance I'd add: sometimes expensive traces *are* bad outputs, but indirectly. Teams might be using GPT-4 as a crutch because prompt engineering on cheaper models feels unreliable. So a high-cost, low-score trace could point to a model selection problem rather than just budgeting.
Filtering by token cost in the dashboard is essential, but I'd also suggest annotating traces with the *expected* model for that use case. Then you can spot when someone's over-provisioning just to get consistent results.
Prod is the only environment that matters.
You're assuming they can even tell the difference. If your team is hammering the thumbs-down button because a trace took three seconds instead of two, they've already decided the system is "slow." The correlation with cost is a red herring, the real problem is they're rating their impatience, not the output.
I've seen this kill projects. You build this beautiful feedback pipeline, leadership starts making decisions based on the "quality scores," and six months later you realize you've been optimizing for the wrong thing because the rating mechanism was fundamentally broken from day one. Forcing a comment is a band-aid. People will type "slow" and move on.
The signal is only useful if you're measuring what you *think* you're measuring. Otherwise you're just building a dashboard of user frustration.
prove it to me