User Feedback
Collect and analyze user feedback on LLM responses. Evaluate output quality, identify trends, and optimize your AI application’s performance based on user satisfaction.
Introduction
User feedback is a feature that allows users to evaluate the responses generated by the LLM. This feedback can be either positive
or negative
, offering crucial insights into the effectiveness and relevance of the LLM’s outputs based on user’s satisfaction.
Why User Feedback
User feedback allows you to:
- Gauge the efficacy of the LLM’s responses.
- Optimize the user experience of your LLM app as you modify your prompts or models based on the received feedback.
- Identify trends in feedback to make informed decisions about model training or fine-tuning.
Quick Start
Option 1: Logging Feedback using Helicone’s Node Package
If you’re using Helicone’s Node package, here’s a simplified example to log feedback:
Option 2: Logging Feedback using Fetch
Without Helicone’s Node package, you can still log feedback using the Fetch API. For detailed documentation, refer to:
Fetch API for Feedback
Not using Helicone’s Node Package? We got you, too.
In some packages or scenarios, you may not be able to retrieve headers to get
the helicone-id
. However, you can still log feedback by supplying a UUID as
the helicone-id
.
Here’s a simple example: