Using question labels to make a RAG chatbot work better
A chatbot can sort incoming questions before it searches for supporting material. The method turns each question into , then uses to predict what kind of question it is. That predicted category becomes metadata.
The metadata helps the chatbot choose more relevant material for retrieval. The goal is to improve the quality of a .
Key points
- Questions are categorized before the chatbot retrieves supporting material.
- are used to represent the meaning of each question.
- predicts the question category.
- The category is stored as metadata for the RAG flow.
- This can help narrow retrieval and may reduce wasted token use.