A few days ago we published an article (https://getdozer.io/blog/llm-chatbot) describing how Dozer could improve hyper-personalization when used together with LLMs, Vector databases, and Langchain.
The article describes a hypothetical bank implementing an LLM-based chatbot and leveraging Dozer to create a unified customer profile and later on passing it to an LLM as a context to hyper-personalize the chatbot.
Based on this article, a complete working sample must be produced. Dozer should be configured to source from multiple datasets (like customer profiles, transactions, etc) and Dozer APIs to be integrated with langchain. A similar use case (as described in the article) of credit card products should be built.
A few days ago we published an article (https://getdozer.io/blog/llm-chatbot) describing how Dozer could improve hyper-personalization when used together with LLMs, Vector databases, and Langchain.
The article describes a hypothetical bank implementing an LLM-based chatbot and leveraging Dozer to create a unified customer profile and later on passing it to an LLM as a context to hyper-personalize the chatbot.
Based on this article, a complete working sample must be produced. Dozer should be configured to source from multiple datasets (like customer profiles, transactions, etc) and Dozer APIs to be integrated with langchain. A similar use case (as described in the article) of credit card products should be built.