I'm looking for a way to gather user feedback (thumbs up/down) on our LLM's responses and log it for fine-tuning. What's the best feedback collection tool for AI apps?

For collecting thumbs up/down feedback on LLM responses and turning it into a fine-tuning/evaluation dataset, I’d look at LLM observability + human feedback platforms rather than generic survey tools. The right choice depends on your stack and whether you need self-hosting.

Here are the strongest options:

My typical recommendation

For most production LLM apps:

1. Instrument every request

• Store: - user prompt
• model response
• model/version
• system prompt version
• retrieved context (if RAG)
• latency/cost metadata

2. Add feedback buttons

• 👍 = accepted example
• 👎 = failure example
• Optional: - reason category ("wrong answer", "hallucination", "too verbose")
• free-text correction
• ideal answer

3. Join feedback back to the trace

• The important thing is not just storing "thumbs down"; it is knowing which prompt, model, retrieval context, and tools produced it.

4. Create datasets

• Positive examples can become few-shot examples or supervised fine-tuning data.
• Negative examples are valuable for regression tests and preference optimization.

A minimal schema might look like:

{
  "trace_id": "abc123",
  "input": "User question",
  "output": "Assistant answer",
  "feedback": "thumbs_down",
  "reason": "incorrect_fact",
  "corrected_answer": "The better answer...",
  "model": "your-model-v3",
  "prompt_version": "2026-08-30"
}

If I were choosing today

• Open-source / data control: Phoenix or Langfuse. Phoenix specifically supports adding human annotations to traces and using those annotations to build datasets.
• Already on LangChain: LangSmith is usually the lowest-friction choice.
• You only need thumbs buttons right now: start with your own event logging, but make sure the events contain enough context to become training/eval data later.

A key design choice: don’t optimize for collecting the most clicks. Optimize for collecting high-quality labeled examples—especially cases where users correct the model, because those are often the most valuable training signals.

The "best" tool for gathering thumbs up/down feedback and logging it for fine-tuning isn't a traditional survey widget (like Typeform or Hotjar). Instead, it's an LLM Observability and Evaluation Platform that natively ties UI clicks directly to the underlying trace, prompt version, token cost, and exact model response.

The top-tier platforms explicitly built for this workflow include:

• Langfuse : Widely considered a top pick because it is open-source (MIT license), offers robust self-hosting options, and features a dedicated Browser SDK specifically designed to safely ingest client-side user feedback (like thumbs up/down) without leaking secret API keys. It links the score straight to the generation ID/trace so you can later filter and export fine-tuning datasets.
• Braintrust : An exceptional enterprise-grade option with an incredible evaluation and logging loop. It natively supports capturing user feedback (scores like 1/0 for thumbs, expected value corrections, and free-form comments) and ties them straight into production traces.
• Arize Phoenix / Weave (by Weights & Biases) / Opik (by Comet) : Strong alternatives if you are already using their ecosystem for experiment tracking or evaluation. They all provide simple SDK hooks to attach binary scores to specific LLM spans.

How the Feedback Loop Works in Practice

Regardless of which tool you pick, a production-ready feedback loop requires a standard implementation flow:

Are you leaning toward a fully open-source/self-hosted tool like Langfuse , or a managed commercial cloud platform ? Let me know your stack (e.g., Python/Next.js, LangChain/LlamaIndex) and I can share a quick code snippet for the integration.

If your goal is specifically “thumbs up/down → tie it to the exact LLM response → analyze bad responses → turn the good/bad examples into a fine-tuning/eval dataset,” my top pick is Langfuse.

My recommendation

1. langfuse.com — best fit for this use case

It treats thumbs-up/down as a score attached directly to the trace that generated the response. You can then filter for negative feedback, inspect the full conversation/model trace, add comments, and use those examples in datasets and evaluations.

A typical flow would be:

User
  ↓
Your LLM app
  ↓
LLM response + Langfuse trace ID
  ↓
👍 / 👎 button
  ↓
"response_rating" score attached to trace
  ↓
Filter 👎 responses
  ↓
Review / label / cluster failures
  ↓
Add selected examples to evaluation or fine-tuning dataset

Langfuse explicitly supports browser-side thumbs-up/down feedback and optional comments, and its feedback data can be used to build datasets and run experiments.

2. humanloop.com — strong alternative

Humanloop is particularly attractive if your emphasis is on human evaluation and prompt/model iteration. Its feedback system maps 👍/👎 into evaluator results and lets you filter logs by bad ratings and use them to improve prompts.

3. braintrust.dev — consider if you want a broader eval/CI platform

Braintrust is more oriented toward a full evaluation workflow—tracing, human review, automated evaluators, datasets, and quality gates—rather than merely collecting feedback.

What I'd implement

Don't store just:

{"feedback": "down"}

Instead, attach feedback to the specific response/trace and capture something like:

{
  "feedback": "down",
  "trace_id": "abc123",
  "user_id": "user_456",
  "model": "your-model",
  "prompt_version": "v17",
  "comment": "It hallucinated the pricing."
}

I'd also capture implicit feedback alongside thumbs: retries, regenerated answers, copied output, edits, abandonment, etc. Explicit thumbs are valuable but tend to have low response rates and can be biased toward dissatisfied users.

One important caveat about fine-tuning

Don't automatically turn every 👍 into a fine-tuning example. A thumbs-up tells you the user liked the response; it doesn't necessarily mean the response is an ideal training target. For 👎 examples, you ideally want a corrected answer or human annotation before using them for supervised fine-tuning.

So I'd use the pipeline:

👍/👎 → collect → review/annotate → curated dataset → eval → fine-tune → regression test

For a typical production AI app, I'd start with Langfuse because it gives you the feedback collection and the tracing/evaluation infrastructure around it, rather than building a separate feedback database and later trying to connect it to your LLM logs.

If you tell me your stack (e.g. Next.js + OpenAI, Python/FastAPI, Vercel AI SDK, etc.), I can show you the simplest architecture and implementation for adding the 👍/👎 buttons.

The best tools for capturing thumbs up/down user feedback and logging it for fine-tuning are LLM observability and tracing platforms that tie user feedback directly to specific trace IDs, spans, and prompt-response pairs.

Instead of building a logging database from scratch, specialized LLM operations platforms provide simple SDK methods to push UI feedback into structured datasets suitable for fine-tuning.

Top Feedback Collection & Logging Tools

Best Practices for Fine-Tuning Preparation

• Capture the Span/Trace ID: When your backend generates an LLM response, pass a unique trace_id or span_id to your frontend UI. When a user clicks thumbs up or down, send that ID back with the rating so the platform anchors the feedback to the exact prompt, context, and generation parameters.
• Collect Qualitative Context: Thumbs up/down gives a binary signal, but negative feedback (thumbs down ) benefits immensely from a quick optional dropdown or text box asking why (e.g., "Inaccurate," "Incomplete," or "Tone issue").
• Filter Before Fine-Tuning: Do not blindly feed all positive and negative reactions into your training pipeline. Use your observability dashboard to filter out low-quality user complaints or ambiguous ratings before exporting the final dataset.

If you can share your current tech stack (e.g., LangChain, custom Python backend, Vercel frontend) or whether you prefer an open-source self-hosted vs. managed cloud tool, I can recommend the exact integration approach for your setup.

Here are top web results for exploring this topic:

PostHog·https://posthog.com**Collecting user feedback** - Docs - PostHog BasicsTracesSpansGenerationsSessionsErrorsToolsEmbeddingsTracking custom propertiesCustom parsersData retention. Integrations. Third-party integrations · Link Session ReplayLink Error Tracking. User f The Training Boss·https://thetrainingboss.com**Thumbs Up** & Down for LLM Responses - The Training Boss Why Thumbs Up / Down Matters. When users interact with an AI chatbot, every response is a chance to learn something: Was the answer correct? Was it useful? Did it follow instructions? Was it too long Reddit·https://www.reddit.com**What** can we do with thumbs up and down in a RAG or document ...I've been researching how AI applications (like ChatGPT or Gemini) utilize the "thumbs up" or "thumbs down" feedback they collect after generating an answer. My main question is: how is this seemingly LinkedIn·https://www.linkedin.com**How** to track LLM user feedback and improve your AI applications Helicone (YC W23). The open-source LLM observability platform for… Published May 14, 2025. + Follow. In today's AI-driven landscape, learning how to effectively track LLM user feedback is crucial for iCertGlobal·https://www.icertglobal.com Implementing user feedback loops and 'thumbs up ' buttons using ...Suggested Questions. Does transitioning to agile framework methodologies improve long term software quality? Software Development · 05-11-2025 Which AI tool for coding has improved your development pr getthematic.com·https://getthematic.com/insights/llms-for-feedback-analytics Which LLM's Are Best For Feedback Analysis? - Getthematic ... right for the objective. Thematic ingests Voice of Customer and Voice of Employee data gathered through surveys, conversations, app reviews and experience management tools. Our pre-processing make Mopinion·https://mopinion.com**Top** 12 AI -powered customer feedback analytics tools in 2026 This article delves into: What are AI-powered customer feedback analytics tools? Why AI matters in customer feedback analytics; How to choose the right AI customer feedback analytics tool; Quick compa Confident AI·https://www.confident-ai.com**Top** 9 LLM Evaluation Tools in 2026 - Confident AI But the tooling landscape is fragmented. Some tools evaluate prompts in isolation. Others focus on a single use case like RAG. A few bolt evaluation onto observability platforms as an afterthought. An Winder.AI·https://winder.ai**User Feedback** in LLM -Powered Applications - Winder.AI Collection: Capture raw signals (text, ratings, edits) alongside metadata (timestamps, user IDs, context). Labeling: Enrich records with categories-issue type, sentiment, relevance flags-via automated BetaTesting·https://blog.betatesting.com**Top Tools** to Get Human Feedback for AI Models - BetaTesting Blog ... responses or annotation data on another, so that high-quality human input flows in quickly from all angles This means you spend less time reinventing the wheel and more time acting on insights tha

If your goal is specifically thumbs-up/down → trace the exact LLM response → turn the best/worst examples into fine-tuning/eval data, my default recommendation is Langfuse.

My pick: Langfuse

langfuse.com is particularly well suited because feedback is modeled as a score attached to the LLM trace. You can send a boolean score such as 1 = thumbs up / 0 = thumbs down directly from your UI, then filter and analyze the underlying conversations.

More importantly for your use case, it isn't just a feedback widget:

• Capture thumbs up/down against the exact response/trace.
• Add free-text feedback or reviewer annotations.
• Filter for negative examples to find failure patterns.
• Build datasets from production traces for evaluation.
• Capture corrected outputs from human reviewers, which can be exported as higher-quality fine-tuning data.
• Run LLM-as-a-judge and automated evaluations on the same traces, so user feedback and automated quality scores live together.
• It's open source/self-hostable, which is attractive if your feedback data contains sensitive customer conversations.

How I'd structure your feedback loop

I'd avoid treating thumbs-down itself as a fine-tuning example. Instead:

User gets LLM response
        ↓
 👍 / 👎 + optional "What was wrong?"
        ↓
Feedback attached to trace
        ↓
Filter 👎 responses
        ↓
Human reviews/corrects them
        ↓
Approved examples → evaluation dataset
                       ↓
                 fine-tuning dataset

That's important because a thumbs-down tells you "this was bad", but not necessarily what the correct answer should have been. Langfuse's corrected-output workflow is designed for exactly that additional step.

Other strong options

Braintrust is particularly compelling if the eventual goal is a sophisticated human-review → dataset → automated-eval → CI quality-gate workflow; its platform emphasizes that loop.

One caveat: don't rely exclusively on thumbs. Explicit feedback tends to have low response rates and can be biased toward dissatisfied users. Also capture implicit signals such as regeneration, copying, editing, abandonment, or escalation.

Bottom line: for a typical AI product starting this today, I'd use Langfuse, put a tiny thumbs component in your UI, associate every vote with the corresponding trace ID, and build your fine-tuning corpus only from reviewed/corrected examples rather than raw votes.

If you tell me your stack (e.g. Next.js + Python/FastAPI + OpenAI, etc.), I can show you the minimal architecture and code for implementing the feedback loop.

The best tools for capturing thumbs up/down user feedback and logging it for LLM fine-tuning are specialized LLM observability and monitoring platforms that feature dedicated feedback APIs and dataset export workflows.

Top platforms for this use case include Helicone (which provides a direct Feedback API to log votes and export fine-tuning datasets), PostHog (with native surveys and trace-attached feedback), and Trubrics (ideal for embedded UI components in Python/Streamlit apps).

Top Tools for LLM Feedback Logging

• Helicone: Best for production proxy-based logging, allowing you to attach scores via custom properties or a Feedback API and export filtered datasets directly for training.
• PostHog: Best if you want product analytics combined with AI observability, using its native surveys feature to tie thumbs up/down right to specific LLM traces.
• Trubrics: Best for rapid prototyping and data apps, offering drop-in UI feedback components that sync user ratings directly to user storage.
• Traceloop: Best for OpenTelemetry-native tracking, helping you manage explicit user satisfaction and evaluations alongside performance metrics.

If you can share what framework or stack you are using for your app (e.g., Python/Streamlit, Next.js, LangChain), I can provide a code snippet for the tool that fits best.

For your specific use case—thumbs up/down on production LLM responses, tied to the exact request/response, then turned into fine-tuning/evaluation data—I'd recommend Langfuse.

Why Langfuse is a strong fit

• Native user feedback: You can send 👍/👎 as boolean scores directly from your frontend and associate them with the response's trace.
• Keeps the full context: The feedback is attached to a trace, so you can inspect the prompt, model output, retrieval/tool calls, latency, etc.—not just a standalone thumbs-down record.
• Easy to turn feedback into datasets: You can identify bad responses, review/annotate them, and build datasets for subsequent experiments.
• Beyond thumbs: Add optional comments, star ratings, categorical labels, or implicit signals such as whether a user retried a request.
• Good for the whole feedback loop: Production traces → user feedback → curated examples → evaluation dataset → test new prompts/models. That's more useful than a feedback widget that merely dumps 👍/👎 into a database.

How I'd structure it

User
  │
  ├── LLM response
  │      └── trace_id = abc123
  │
  └── 👍 / 👎
           │
           ▼
       Langfuse
           │
           ├── trace: prompt + response + metadata
           ├── score: user-feedback = 1/0
           └── optional comment
                    │
                    ▼
             Review negative examples
                    │
                    ▼
              Curated dataset
                    │
                    ▼
          Fine-tuning / evals / prompt tests

The particularly nice part is that Langfuse explicitly supports browser-side score ingestion for things like thumbs up/down, while keeping the secret API key off the client.

What about alternatives?

Braintrust is worth considering if you're already building a fairly sophisticated evaluation/experimentation platform. Langfuse would be my default pick if your immediate requirement is "capture production feedback and build an evaluation/fine-tuning loop around it."

I wouldn't choose Humanloop for a new implementation: although its documentation describes essentially this exact thumbs-up/down workflow, its current docs state that the Humanloop platform was sunset on September 8, 2025.

One important caveat: don't blindly fine-tune on thumbs-down examples. A 👎 is a quality signal, not necessarily the corrected answer. Ideally, use it to find examples for human review, then store something like:

prompt → bad response → user feedback → corrected/ideal response

That gives you much more valuable training data.

If you tell me your stack (e.g. Next.js + OpenAI, Python/FastAPI, Vercel AI SDK, etc.), I can show you the minimal implementation for collecting the thumbs and producing a fine-tuning-ready dataset.

For your specific use case—thumbs up/down → associate with the exact LLM response → analyze failures → turn good/bad examples into fine-tuning/eval data—I'd start with Langfuse.

My recommendation

1. Langfuse — best fit for most teams

• Built-in user feedback as scores, including thumbs up/down and comments.
• Feedback is linked directly to the underlying LLM trace, so you retain the prompt, response, model, latency, metadata, etc.
• You can filter feedback, manually annotate examples, and turn production traces into datasets for evaluation.
• Open source and self-hostable, which is a major advantage if your feedback data is sensitive.
• Has Python/JS SDKs and a Browser SDK for capturing frontend feedback.

2. Braintrust — excellent if you want a more integrated eval/production workflow

Braintrust captures production requests as traces, lets you attach human feedback/scores, and can turn production data into evaluation datasets. It's particularly attractive if you expect to build a sophisticated automated evaluation and regression-testing system around the feedback loop.

What I'd build

Don't just log:

👍 / 👎

Instead, capture something like:

{
  "trace_id": "abc123",
  "user_id": "user_456",
  "prompt": "...",
  "response": "...",
  "model": "your-model",
  "feedback": "negative",
  "feedback_reason": "incorrect",
  "comment": "The second claim is wrong",
  "timestamp": "...",
  "metadata": {
    "app_version": "1.8.2",
    "prompt_version": "support-v12"
  }
}

The important architectural decision is linking the feedback to a trace ID, rather than treating feedback as a standalone database event. Langfuse explicitly supports this pattern: its user feedback is stored as scores associated with traces.

Then your loop becomes:

User response → 👍/👎 → trace → filter bad examples → human review → curated dataset → eval/fine-tuning

One caveat: don't automatically fine-tune on every thumbs-up/down. Thumbs-down is a useful discovery signal, but you'll want to classify/review examples first. A thumbs-down can mean factual error, bad formatting, refusal, irrelevant answer, latency, or simply that the user wanted something different. Langfuse itself recommends combining explicit feedback with implicit signals and evaluation.

Bottom line: If you're building this from scratch today, I'd choose Langfuse unless you already know you want Braintrust's more evaluation-centric workflow. For a simple thumbs-up/down feedback loop that can grow into a serious LLM evaluation/data pipeline, Langfuse is a particularly strong fit.

The information for the question you asked has been looked up and is now ready to be viewed. Here's a response to the question:

Collecting thumbs up/down feedback to log and turn into a fine-tuning dataset requires an LLM observability and evaluation platform that ties user scores directly to execution traces, prompts, and completions. A couple of isolated UI buttons are useless if you don't capture the underlying context.

The best tools for this pipeline fall into a few clear categories depending on your stack:

• Braintrust : Best end-to-end evaluation and observability platform. It tightly integrates human review/ratings with tracing, dataset management, and CI/CD quality gates so feedback flows straight into your improvement loops.
• Langfuse : Best open-source (and self-hostable) option. It provides lightweight SDKs for tracing LLM calls, recording user feedback/annotations, and exporting those datasets cleanly.
• LangSmith : Best if you are already built heavily on LangChain or want seamless proxy-level tracing paired with robust dataset generation from production logs.
• W&B Weave : Best for ML-heavy teams using Weights & Biases. It logs trace trees with a single line of code and makes capturing user or expert feedback effortless.
• Helicone : Best lightweight proxy-based monitor if you just want to capture request/response data and append user feedback metadata headers quickly without heavy code refactoring.

Key Tips for Logging Fine-Tuning Data from Thumbs:

1. Never log raw thumbs alone: A random thumbs-down could be a fat-finger or a frustrated user. Try to pop a lightweight micro-survey (e.g., "Inaccurate?", "Too long?") upon a thumbs-down click.
2. Capture the full trace: Ensure your feedback payload saves the trace_id or run_id alongside the exact prompt, retrieved context (if RAG is used), and model generation.
3. Filter the noise: Build a triage step before pushing data into your fine-tuning bucket (like DPO preference pairs or SFT datasets) to weed out ambiguous or low-quality feedback.

If you share what framework/stack you are using (e.g., LangChain, custom Python, Vercel/Node) and whether you prefer open-source vs. managed SaaS , I can recommend the exact integration path.