https://docs.ai.gwdg.de/en/categories/ai-transparency/index.htmlHugoenhttps://docs.ai.gwdg.de/en/user/ai-services/arcana/transparency/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.ai.gwdg.de/en/user/ai-services/arcana/transparency/index.htmlGoal Typical LLMs have the advantage, but also the problem, of having been trained on an incredible amount of data. This means that they know a lot, but are often unable to answer very specific questions. In these cases, LLMs are very prone to hallucination, which means that they basically make things up. One way to improve the performance of LLMs for very specific questions is to use Retrieval-Augmented-Generation (RAG). Here, users provide custom documents that contain the knowledge base they want to ask questions about later. Before an LLM responds to a user’s query, the most relevant documents previously provided by the user are retrieved and provided to the LLM as additional context.