Large Language Models (LLMs) like ChatGPT excel at answering general questions but often struggle to incorporate domain-specific knowledge or data beyond their training cutoff date. One effective solution to this limitation is “Retrieval Augmented Generation” (RAG). RAG enhances in-context learning by allowing users to include information retrieved from documents within a prompt. This enables the language model to make inferences based on previously unseen information. However, traditional RAG operates under the assumption that every query requires additional context, even when it may not be necessary. A more refined approach involves enabling the LLM to determine when additional context is needed.

Large Language Models (LLMs) like ChatGPT excel at answering general questions but often struggle to incorporate domain-specific knowledge or data beyond their training cutoff date. One effective solution to this limitation is “Retrieval Augmented Generation” (RAG). RAG enhances in-context learning by allowing users to include information retrieved from documents within a prompt. This enables the language model to make inferences based on previously unseen information. However, traditional RAG operates under the assumption that every query requires additional context, even when it may not be necessary. A more refined approach involves enabling the LLM to determine when additional context is needed.

Large Language Models (LLMs) like ChatGPT excel at answering general questions but often struggle to incorporate domain-specific knowledge or data beyond their training cutoff date. 

 This enables the language model to make inferences based on previously unseen information. 

 This enables the language model to make inferences based on previously unseen information.  This enables the language model to make inferences based on previously unseen information. This is a link