What does using an LLM as a reasoning engine refer to?

Using an LLM (Large Language Model) as a reasoning engine relates to its ability to process context and generate responses based on that context. This is particularly enhanced by techniques such as Retrieval-Augmented Generation (RAG), where the model is not only generating text based on its training data but also retrieving relevant information from external data sources to inform its output. This ability to combine retrieval with generation allows for more nuanced and context-aware responses, making the model a powerful tool for tasks that require understanding and reasoning over large sets of data.

In this context, while the model can serve as a source of information or be applied in games for decision-making, the term "reasoning engine" specifically emphasizes the model's role in synthesizing and reasoning about data. This is distinct from referring to it simply as a source of information or limiting its application to specific scenarios like gaming. Furthermore, linking it to RAG accurately captures the essence of enhancing reasoning capabilities with external contextual data.