User Feedback for Chatbots: Enhancing Performance and Optimization

In the realm of AI-driven chatbots, particularly those employing Large Language Models (LLMs) as their foundational technology, one crucial factor that often determines the success of these applications is the integration of user feedback. This blog post delves into the importance of user feedback in improving a chatbot’s performance, as well as the significance of storing question-answer pairs and implementing preference models for optimization.

The Role of User Feedback in Chatbots

A chatbot’s primary goal is to provide accurate and relevant responses to users while maintaining an engaging conversation. However, not all generated answers will be correct or satisfactory. This is where user feedback comes into play. By allowing users to rate the quality of responses, we can gain valuable insights into which parts of our LLM-driven chatbot are functioning well and which require improvement.

Storing Question-Answer Pairs for Analysis

One effective way to gather data on a chatbot’s performance is by storing each question-answer pair alongside the user feedback provided. This approach enables us to analyze trends in response accuracy and identify areas where our chatbot may be struggling. Additionally, this information can be used for recall benchmarking, which compares the chatbot’s performance over time.

Implementing Thumbs Up/Down Mechanism for Response Scoring

A simple yet powerful mechanism to collect user feedback is by incorporating a thumbs up or thumbs down feature in our chatbot interface. This allows users to quickly indicate whether they found the response helpful or not, providing us with valuable data on the overall accuracy of our LLM-driven chatbot. By assigning numerical scores based on user feedback, we can filter out underperforming text chunks and focus on improving their quality.

Supervised Fine Tuning for Optimization

In the short to medium term future, advancements in supervised fine tuning techniques will enable us to optimize our chatbots more effectively than ever before. This method involves training a model using labeled data, which can be derived from the question-answer pairs collected through user feedback. By leveraging this approach, we can tailor our LLM-driven chatbot to better suit the needs of its users and deliver increasingly accurate responses over time.

Conclusion

In conclusion, the integration of user feedback is essential for continuously improving the performance and relevance of LLM-driven chatbots. By storing question-answer pairs and implementing a thumbs up/down mechanism, we can gain valuable insights into our chatbot’s accuracy and identify areas requiring improvement. Moreover, as supervised fine tuning techniques evolve, we will be able to optimize our chatbots more effectively, ensuring they remain an indispensable tool in the world of AI-powered communication.

• Human Intervention: None

Facts Used:

• When building your LLM (large language model) driven RAG (retrieval augmented generation) chatbot, you want user feedback to improve its performance.
• Not all answers coming from the chatbot will be right. It’s important to store the question and the response for further analysis along with a mechanism like thumbs up/down on the response. This lets you “score” your text chunk accuracy and eventually filter out chunks that are not producing good results. Add a key in your mongo document for this feedback.
• Storing the question/answer pairs can also be used later for recall benchmarking and supervised fine tuning an instruct-tuned LLM on your data set. Currently supervised fine tuning and direct preference optimization model training techniques are expensive and painful to do, but this will change in the short to medium term future.