How to Track LLM User Feedback to Improve Your AI Applications

Lina Lam· May 1, 2025

In today's AI-driven landscape, learning how to effectively track LLM user feedback is crucial for improving performance and driving higher user satisfaction.

Every user interaction provides valuable insights that can help you refine your AI's responses to better serve your customers' needs.

In this article, we will show you how to use LLM feedback tracking tools like Helicone to collect, analyze, and implement user feedback for continuous improvement of your AI applications.

Table of Contents

• Why is Tracking User Feedback Critical for LLM Applications?
• The Feedback Collection Framework
• Turning User Feedback into Training Datasets
• Success Stories
• Implementation Best Practices
• Useful Resources

Why is Tracking User Feedback Critical for LLM Applications?

Creating a continuous user feedback loop is essential for any successful software application. This applies to LLM applications as well.

Collecting LLM user feedback creates a virtuous cycle of improvement through five critical stages:

1. User Interaction - Users engage with your LLM application
2. Feedback Collection - You gather structured data on response quality.
3. Pattern Analysis - You identify trends and opportunities for improvement
4. Dataset Creation - You create specialized training datasets based on feedback
5. Prompt Optimization - You fine-tune your models or update your prompts accordingly

This systematic approach is useful for building better AI products while reducing costs associated with poor user experiences.

A study published by Google DeepMind in April 2024 showed that aligning LLM outputs with user feedback led to a significant increase in positive user interactions, as evidenced by a larger positive playback rate gain.

Other studies also showed that incorporating user feedback into LLM application development leads to more efficient customer service operations. For example, Gorgias reported a 52% faster resolution of support tickets. Meanwhile, KPMG's Global CEE Report 2023-24 reported a 30% reduction in operational costs.

The Feedback Collection Framework

Helicone, an open-source observability platform for LLM applications, provides several powerful methods to gather, organize, and analyze user feedback for your LLM applications.

Method 1: Implementing the Feedback API

The most direct way to log user feedback is through Helicone's dedicated Feedback API:

import requests

# First, make your LLM call
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Write a short poem about AI"}],
    extra_headers={
        "Helicone-Auth": f"Bearer {HELICONE_API_KEY}"
    }
)

# Get the Helicone request ID
helicone_id = response.response.headers.get("helicone-id")

# Log user feedback (true for positive, false for negative)

feedback_url = f"https://api.helicone.ai/v1/request/{helicone_id}/feedback"
headers = {
    "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
    "Content-Type": "application/json"
}
data = {
    "rating": True  # True for positive, False for negative
}

requests.post(feedback_url, headers=headers, json=data)

This approach allows you to capture binary feedback (positive/negative) that's directly tied to specific LLM interactions, creating a clear connection between user sentiment and actual LLM responses.

Method 2: Using Custom Properties

For more nuanced feedback collection, Helicone's custom properties allows you to attach custom metadata to your LLM requests.

Simply add a Helicone auth-header, then a header for each custom property you want to track:

client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Write a product description for a coffee maker"}],
    extra_headers={
        "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
        "Helicone-Property-Feedback-Rating": "4",  # On a scale of 1-5
        "Helicone-Property-Feedback-Comment": "Good but too lengthy",
        "Helicone-Property-User-Type": "content-marketer"
    }
)

Custom properties help you:

• Capture numeric ratings beyond binary feedback
• Include qualitative feedback comments
• Segment feedback by user types, features, or use cases
• Track performance across different environments (development, staging, production)

Method 3: Advanced User Metrics Tracking

To go one step further, you can monitor your users' interactions with your AI models to gain deeper insights into usage patterns and their satisfaction levels.

Tracking user metrics in Helicone is similar to tracking custom properties. Simply add a Helicone auth-header (if you haven't already), then the header helicone-user-id: <user_id> for the user you want to track.

client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Summarize this article about AI trends"}],
    extra_headers={
        "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
        "Helicone-User-Id": "user_12345"  # Associate request with specific user
    }
)

By tracking user metrics, you can:

• Analyze per-user request volumes and frequencies
• Track costs associated with individual users
• Identify power users and their behavior patterns
• Detect usage anomalies that might indicate problems
• Correlate feedback with usage intensity

This user-level data provides you with the context and granularity to interpret the feedback collected and prioritize improvements that benefit your most valuable users.

Turning User Feedback into Training Datasets

Once you've collected sufficient feedback, you now have valuable training data for improving your LLM applications. Let's look at how:

Step 1: Filtering and Exporting Your Feedback Data

First, filter your LLM request data based on factors such as:

• Positive vs. negative feedback
• Specific feature usage
• User segments
• Time periods

This specialized dataset represents real-world interactions, which can now be exported from Helicone's dashboard or API to drive meaningful, targeted improvements.

Step 2: Identifying Actionable Insights

With data in hand, analyze your feedback data to identify:

• Common pain points or issues associated with negative feedback
• Highly successful interactions from positive feedback
• How performance varies across different user segments
• Feature-specific feedback patterns

The goal is to discover actionable insights that can guide very specific optimization efforts.

Step 3: Creating Specialized Training Datasets

Based on your analysis, create specialized datasets tailored to your specific improvement goals.

Here's a video on how to create a dataset using Helicone's UI - you can also create a dataset programmatically for more advanced use cases.

Success Stories

Journalist AI: Subscription-Based Feedback Segmentation

Journalist AI is a platform that automates content creation for writers. They use Custom Properties to segment feedback by subscription plan.

Their feedback collection strategy helps them:

• Compare content satisfaction between free and paid users
• Identify which features drive paid subscriptions
• Track costs-to-value ratio for different user tiers
• Target marketing efforts for high-value features

This approach has allowed them to increase their premium conversion rate by 22% in just three months.

Greptile: Repository-Specific Performance Tracking

Greptile helps users search and analyze text data from various sources. They use custom properties to track feedback by repository:

This strategic approach allows them to:

• Measure satisfaction with results from different data sources
• Track performance metrics (latency, costs) by repository
• Identify which repositories need quality improvements
• Understand user search patterns across data sources

Since implementing repository-specific tracking, they've been able to optimize their system for specific data sources, improving both response quality and speed.

Implementation Best Practices

To maximize the value of your feedback collection:

1. Be consistent with property naming - Use standardized naming conventions for Custom Properties
2. Collect feedback at the right time - Ask for feedback immediately after users interact with AI responses when the experience is fresh
3. Keep the feedback process simple - High completion rates come from easy, frictionless feedback mechanisms
4. Balance quantitative and qualitative data - Numbers tell you what's happening; comments tell you why
5. Acknowledge and reward user contributions - Let users know when their feedback has led to specific improvements

Going beyond feedback collection? We recommend reading how to implement LLM observability for production.

Useful Resources

• Doc: Setting up Custom Properties

• Doc: Using Custom Properties for Segmentation

• How to Implement LLM Observability for Production with Helicone