Content revision 4e771eaf0390cc87b6c7c6fd8469f3508f6ceeec7119472e6c1ccf17e2e1e5d2

## Interpreting the customer’s request

Stride Support
Source ID stride:matching-language-to-the-support-domain
Original https://www.henryw.me/#/stride#matching-language-to-the-support-domain

Product names and policy terms can be useful exact matches, while a customer’s description of what they need may use quite different words. We combined BM25, which ranks word matches, with BGE embeddings, numerical representations that capture related meaning. Product and policy search normalise and blend the two scores, making the balance between wording and meaning adjustable. Recommendations add another kind of evidence. Similarity between product descriptions helps find alternatives, while co-purchases and customer activity reveal which items tend to be useful together. Combining these signals supports both a request for something similar and a recommendation based on purchase history. The wording also carries information about how to respond.

We fine-tuned multilingual BERT (mBERT) for sentiment classification and compared it with a bidirectional LSTM and a Transformer encoder trained from scratch on the SST-2 sentiment dataset. The chosen classifier supplies a per-message cue that the response model uses to adjust empathy and concision. A separate embedding-based check recognises the support domain and guides unrelated questions towards available capabilities. We evaluated policy retrieval, tool routing and complete chatbot responses separately, alongside the sentiment-model comparisons. Each addresses a different question. Did retrieval find the relevant passage, did the model select the appropriate operation, and did those results lead to a useful response? This made it possible to examine how each model and retrieval method contributed to the conversation as a whole.