Human feedback is a crucial part of developing your LLM app or agent.
It allows your users to provide direct feedback on the interaction, which can be used to improve the performance and accuracy of your system.
By enabling data persistence, each message sent by your application will be accompanied by thumbs up and thumbs down icons. Users can also add a text comment to their feedback for more detailed input.
Feedback with comment
Dataset Creation: Feedback interactions implicitly generate valuable training data to improve the agent’s responses over time.
Accuracy Measurement: Feedback scores enable objective measurement and comparison of different agent versions, facilitating continuous model improvement.
User-Centric Development: Direct feedback promotes a user-centric approach, ensuring the model evolves to meet user needs and expectations.
Training and Fine-Tuning: Human feedback allows for direct model training and fine-tuning based on specific interactions.
To use human feedback, you first need to enable data persistence.
Once data persistence is enabled, each message sent by your application will be accompanied by thumbs up and thumbs down icons. Users can click these icons to provide feedback on the interaction.
Human feedback is a powerful tool for improving the performance of your LLM app. By enabling data persistence and collecting feedback, you can create a dataset that can be used to improve the system’s accuracy.