What technique should be used by a software company to enable its AI chatbot to give up-to-date responses without retraining?

Retrieval-augmented generation (RAG) is the most suitable technique for enabling an AI chatbot to provide up-to-date responses without the need for retraining. RAG combines the strengths of retrieval-based and generation-based methods. It allows the chatbot to access a dynamic and up-to-date knowledge base or database of information, retrieving relevant documents or data at the moment a query is made. This retrieval process enables the chatbot to incorporate the latest information into its responses, ensuring that users receive accurate and current answers.

This approach is particularly beneficial in scenarios where static models may not reflect the most recent developments or changes in knowledge, as RAG leverages external data sources to enhance its responses. By combining generative capabilities with retrieval techniques, RAG effectively bridges the gap between the limitations of pre-trained models and the need for real-time accuracy, providing an efficient solution for keeping chatbots relevant and informed.